319 Quantization Jobs - Page 5

Sprinklr is a leading enterprise software company for all customer-facing functions. With advanced AI, Sprinklr's unified customer experience management (Unified-CXM) platform helps companies deliver human experiences to every customer, every time, across any modern channel. Headquartered in New York City with employees around the world, Sprinklr works with more than 1,000 of the world’s most valuable enterprises — global brands like Microsoft, P&G, Samsung and more than 50% of the Fortune 100. Learn more about our culture and how we make our employees happier through The Sprinklr Way. Responsibilities Job Description Set Research Direction: Define and execute a forward-looking ML research agenda aligned with company strategy and business objectives and technological innovation. Drive Key Research in relevant areas of Sprinklr: Self-learning AI Agents, Auto Evaluation of AI Agents, Taxonomy discovery and quality, End-to-end voice models, Multi-linguality, Multi-modality, Domain-Specific finetuning and alignment, etc. Advance the State of the Art: Guide research initiatives in areas such as deep learning, generative models, NLP, and reinforcement learning. Encourage and support filing patents and publications in top-tier venues (NeurIPS, ICML, ACL, CVPR, IEEE etc.). Bridge Research and Product: Collaborate with engineering and product teams to transition research into real-world applications. Champion best practices in experimentation, reproducibility, and scalability. Lead and Grow the Team: Mentor and manage a high-performing team of ML researchers and engineers. Foster a culture of curiosity, rigor, and excellence. Act as a Thought Leader: Stay ahead of emerging trends and shape the company's position in the global AI landscape. Evangelism: Serve as a subject matter expert internally and externally. Represent the company in academic and industry events, talks, and panels. Qualifications Deep expertise in modern ML techniques, especially large-scale learning, generative models, or foundational models. Experienced with synthetic dataset generation for production and quantization. PhD in Computer Science, Machine Learning, or a related field. 8+ years of experience in ML/AI, including 4+ years in a leadership role. Proven track record of impactful research contributions, including publications, patents, or open-source work. Strong knowledge of cloud platform technologies and MLops tools such as CUDA, K8s, Docker, PyTorch, TensorRT, etc. Strong leadership and communication skills, with experience managing senior researchers and cross-functional collaboration. Ability to align research investments with business strategy and measurable outcomes. Why You'll Love Sprinklr: We're committed to creating a culture where you feel like you belong, are happier today than you were yesterday, and your contributions matter. At Sprinklr, we passionately, genuinely care. For full-time employees, we provide a range of comprehensive health plans, leading well-being programs, and financial protection for you and your family through a range of global and localized plans throughout the world. For more information on Sprinklr Benefits around the world, head to https://sprinklrbenefits.com/ to browse our country-specific benefits guides. We focus on our mission: We founded Sprinklr with one mission: to enable every organization on the planet to make their customers happier. Our vision is to be the world’s most loved enterprise software company, ever. We believe in our product: Sprinklr was built from the ground up to enable a brand’s digital transformation. Its platform provides every customer-facing team with the ability to reach, engage, and listen to customers around the world. At Sprinklr, we have many of the world's largest brands as our clients, and our employees have the opportunity to work closely alongside them. We invest in our people: At Sprinklr, we believe every human has the potential to be amazing. We empower each Sprinklrite in the journey toward achieving their personal and professional best. For wellbeing, this includes daily meditation breaks, virtual fitness, and access to Headspace. We have continuous learning opportunities available with LinkedIn Learning and more. EEO - Our philosophy: Our goal is to ensure every employee feels like they belong and are operating in a judgment-free zone regardless of gender, race, ethnicity, age, and lifestyle preference, among others. We value and celebrate diversity and fervently believe every employee matters and should be respected and heard. We believe we are stronger when we belong because collectively, we’re more innovative, creative, and successful. Sprinklr is proud to be an equal-opportunity workplace and is an affirmative-action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. See also Sprinklr’s EEO Policy and EEO is the Law.

Description JOB RESPONSIBILITY : Collaborate with cross-functional teams, including data scientists and product managers, to acquire, process, and manage data for AI/ML model integration and optimization. Design and implement robust, scalable, and enterprise-grade data pipelines to support state-of-the-art AI/ML models. Debug, optimize, and enhance machine learning models, ensuring quality assurance and performance improvements. Operate container orchestration platforms like Kubernetes, with advanced configurations and service mesh implementations, for scalable ML workload deployments. Design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment. Engage in advanced prompt engineering and fine-tuning of large language models (LLMs), focusing on semantic retrieval and chatbot development. Document model architectures, hyperparameter optimization experiments, and validation results using version control and experiment tracking tools like MLflow or DVC. Research and implement cutting-edge LLM optimization techniques, such as quantization and knowledge distillation, ensuring efficient model performance and reduced computational costs. Collaborate closely with stakeholders to develop innovative and effective natural language processing solutions, specializing in text classification, sentiment analysis, and topic modeling. Stay up-to-date with industry trends and advancements in AI technologies, integrating new methodologies and frameworks to continually enhance the AI engineering function. Contribute to creating specialized AI solutions in healthcare, leveraging domain-specific knowledge for task adaptation and : Minimum education: Bachelors degree in any Engineering Stream Specialized training, certifications, and/or other special requirements: Nice to have Preferred education: Computer : Minimum relevant experience - 4+ years in AI AND COMPETENCIES Skills : Advanced proficiency in Python with expertise in data science libraries (NumPy, Pandas, scikit-learn) and deep learning frameworks (PyTorch, TensorFlow) Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) and prompt engineering techniques Experience with big data processing using Spark for large-scale data analytics Version control and experiment tracking using Git and MLflow Software Engineering & Development: Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing. DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations. LLM Infrastructure & Deployment: Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management. MLOps & Deployment: Utilization of containerization strategies for ML workloads, experience with model serving tools like TorchServe or TF Serving, and automated model retraining. Cloud & Infrastructure: Strong grasp of advanced cloud services (AWS, GCP, Azure) and network security for ML systems. LLM Project Experience: Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security. General Skills: Python, SQL, knowledge of machine learning frameworks (Hugging Face, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP. Experience in creating LLD for the provided architecture. Experience working in microservices based Expertise : Strong mathematical foundation in statistics, probability, linear algebra, and optimization Deep understanding of ML and LLM development lifecycle, including fine-tuning and evaluation Expertise in feature engineering, embedding optimization, and dimensionality reduction Advanced knowledge of A/B testing, experimental design, and statistical hypothesis testing Experience with RAG systems, vector databases, and semantic search implementation Proficiency in LLM optimization techniques including quantization and knowledge distillation Understanding of MLOps practices for model deployment and Competencies : Strong analytical thinking with ability to solve complex ML challenges Excellent communication skills for presenting technical findings to diverse audiences Experience translating business requirements into data science solutions Project management skills for coordinating ML experiments and deployments Strong collaboration abilities for working with cross-functional teams Dedication to staying current with latest ML research and best practices Ability to mentor and share knowledge with team members (ref:hirist.tech)

https://pmspace.ai/ Company Profile: At pmspace.ai, we’re building next-generation AI tools for project management intelligence. Our platform leverages graph databases, NLP, and large language models (LLMs) to transform complex project data into actionable insights. Join us to pioneer cutting-edge solutions in a fast-paced, collaborative environment. Role Overview We seek a Python Developer with expertise in graph databases (Neo4j), RAG pipelines, and vLLM optimization. You’ll design scalable AI systems, enhance retrieval-augmented workflows, and deploy high-performance language models to power our project analytics engine. Key Responsibilities Architect and optimize graph database systems (Neo4j) to model project knowledge networks and relationships. Build end-to-end RAG (Retrieval-Augmented Generation) pipelines for context-aware AI responses. Implement and fine-tune vLLM for efficient inference of large language models (LLMs). Develop Python-based microservices for data ingestion, processing, and API integrations (FastAPI, Flask). Collaborate with ML engineers to deploy transformer models (e.g., BERT, GPT variants) and vector databases. Monitor system performance, conduct A/B tests, and ensure low-latency responses in production. Required Skills Proficiency in Python and AI/ML libraries (PyTorch, TensorFlow, Hugging Face Transformers). Hands-on experience with graph databases, especially Neo4j (Cypher queries, graph algorithms). Demonstrated work on RAG pipelines (retrieval, reranking, generation) using frameworks like LangChain or LlamaIndex. Experience with vLLM or similar LLM optimization tools (quantization, distributed inference). Knowledge of vector databases (e.g., FAISS, Pinecone) and embedding techniques. Familiarity with cloud platforms (AWS/GCP/Azure) and containerization (Docker, Kubernetes). Job Type: Full-time Pay: ₹5,000.00 - ₹7,000.00 per month Schedule: Day shift Work Location: Remote Expected Start Date: 01/08/2025

Position: AI Lead - Generative & Agentic Systems Availability: Immediate Location: Pune (Willing to relocate is fine) Experience: Min 8+ Yrs. Must Have Skills - AI, ML, Agentic AI, LLMs, RAG, Python, R. Key Responsibilities: Build and deploy LLM-powered agents for fintech-specific use cases. Design agentic workflows using LangGraph, AutoGen, or LangChain Agents. Implement secure RAG pipelines with private embeddings and vector stores. Integrate LLMs (GPT-4, Claude, Gemini) with financial knowledge bases. Ensure data privacy and prompt-level guardrails for financial data. Monitor performance, hallucination risk, and model cost. Required Skills: Experience with LLMs and open-source models (GPT-4, Claude, LLaMA). Proficiency in agentic architectures and LLM tool use. Hands-on with LangChain, LangGraph, or AutoGen. Knowledge of RAG architecture and vector stores. - Strong in Python, FastAPI, Docker. Preferred: Experience on .Net Familiarity with LoRA, quantization, Hugging Face Transformers. Knowledge of financial regulations (MiFID II, Basel III, etc.). AI explainability and human-in-the-loop review flows.

AI Research Intern – AryaXAI AI Alignment Labs Commitment: Full-time internship (6 months; potential extension or full-time offer) Start Date: Rolling About AryaXAI AI Alignment Labs AryaXAI AI Alignment Labs, based out of Mumbai, India and Paris, France, is the alignment and explainability division of AryaXAI.com, part of Aurionpro Solutions. We work on AI interpretability and trustworthiness for mission-critical sectors. Our open-source initiatives include the xai_evals benchmarking suite and the DLBacktrace explainability framework, both designed to make AI more transparent, reliable, and aligned with human values. What You’ll Do Collaborate closely with our research and engineering teams on one of the areas: Library Development: Architect and enhance open-source Python tooling for alignment, explainability, uncertainty quantification, robustness, and machine unlearning. Model Benchmarking: Conduct rigorous evaluations of LLMs and deep networks under domain shifts, adversarial conditions, and regulatory constraints. Explainability & Trust: Design and implement XAI techniques (LRP, SHAP, Grad-CAM, Backtrace) across text, image, and tabular modalities. Mechanistic Interpretability: Probe internal model representations and circuits—using activation patching, feature visualization, and related methods—to diagnose failure modes and emergent behaviors. Uncertainty & Risk: Develop, implement, and benchmark uncertainty estimation methods (Bayesian approaches, ensembles, test-time augmentation) alongside robustness metrics for foundation models. Research Contributions: Author and maintain experiment code, run systematic studies, and co-author whitepapers or conference submissions. General Required Qualifications Strong Python expertise: writing clean, modular, and testable code. Theoretical foundations: deep understanding of machine learning and deep learning principles with hands-on experience with PyTorch. Transformer architectures & fundamentals: comprehensive knowledge of attention mechanisms, positional encodings, tokenization and training objectives in BERT, GPT, LLaMA, T5, MOE, Mamba, etc. Version control & CI/CD: Git workflows, packaging, documentation, and collaborative development practices. Collaborative mindset: excellent communication, peer code reviews, and agile teamwork. Preferred Domain Expertise (Any one of these is good) : Explainability: applied experience with XAI methods such as SHAP, LIME, IG, LRP, DL-Bactrace or Grad-CAM. Mechanistic interpretability: familiarity with circuit analysis, activation patching, and feature visualization for neural network introspection. Uncertainty estimation: hands-on with Bayesian techniques, ensembles, or test-time augmentation. Quantization & pruning: applying model compression to optimize size, latency, and memory footprint. LLM Alignment techniques: crafting and evaluating few-shot, zero-shot, and chain-of-thought prompts; experience with RLHF workflows, reward modeling, and human-in-the-loop fine-tuning. Post-training adaptation & fine-tuning: practical work with full-model fine-tuning and parameter-efficient methods (LoRA, adapters), instruction tuning, knowledge distillation, and domain-specialization. Additional Experience (Nice-to-Have) Publications: contributions to CVPR, ICLR, ICML, KDD, WWW, WACV, NeurIPS, ACL, NAACL, EMNLP, IJCAI or equivalent research experience. Open-source contributions: prior work on AI/ML libraries or tooling. Domain exposure: risk-sensitive applications in finance, healthcare, or similar fields. Performance optimization: familiarity with large-scale training infrastructures. What We Offer Real-world impact: address high-stakes AI challenges in regulated industries. Compute resources: access to GPUs, cloud credits, and proprietary models. Competitive stipend: with potential for full-time conversion. Authorship opportunities: co-authorship on papers, technical reports, and conference submissions.

Company Overview : At Growth Loops Technology, we are at the forefront of AI innovation, leveraging cutting-edge machine learning and natural language processing (NLP) techniques to build transformative products. We are looking for an experienced and passionate LLM Engineer to join our team and help us develop and optimize state-of-the-art language models that push the boundaries of what's possible with AI. Job Description : As an LLM Engineer, you will be responsible for designing, building, and fine-tuning large-scale language models (LLMs) to solve complex real-world problems. You will work alongside data scientists, machine learning engineers, and product teams to ensure our models are not only accurate but also efficient, scalable, and capable of handling diverse use cases. The ideal candidate will have a strong background in natural language processing, deep learning, and large-scale distributed systems. You should be passionate about advancing the field of AI and have hands-on experience with LLMs, such as GPT, BERT, or similar architectures. Key Responsibilities : Model Development : Design, develop, and fine-tune large language models (LLMs) for various applications, including text generation, translation, summarization, and question answering. Research & Innovation : Stay up to date with the latest advancements in NLP and LLM architectures, and propose new approaches to improve model performance and efficiency. Optimization : Implement optimization techniques to reduce computational resource requirements and improve model inference speed without sacrificing accuracy or performance. Scalability : Develop strategies for training and deploying models at scale, ensuring robustness and reliability in production environments. Collaboration : Work closely with cross-functional teams (data science, software engineering, product) to integrate LLM capabilities into our products and solutions. Evaluation & Benchmarking : Establish and maintain rigorous testing, validation, and benchmarking procedures to assess model quality, performance, and generalization. Model Explainability : Develop methods to improve the interpretability and explainability of language models, ensuring that outputs can be understood and trusted by end-users. Qualifications : Education : Bachelor's, Master's in Computer Science, Artificial Intelligence, Machine Learning, or a related field. Experience : Proven experience (1+ years) working with NLP, deep learning, and LLM architectures (e.g., GPT, BERT, T5, etc.). Expertise in programming languages such as Python and experience with machine learning frameworks like TensorFlow, PyTorch, or JAX. Solid understanding of transformer models, attention mechanisms, and the architecture of large-scale neural networks. Experience with distributed computing, GPU acceleration, and cloud-based machine learning platforms (e.g., AWS, GCP, Azure). Familiarity with model deployment tools and practices (e.g., TensorFlow Serving, or Hugging Face). Skills : Strong problem-solving skills and ability to work on complex, ambiguous tasks. Solid understanding of model evaluation metrics for NLP tasks. Experience with large datasets and parallel computing for training and fine-tuning LLMs. Familiarity with optimization techniques such as pruning, quantization, or knowledge distillation. Excellent communication skills, both written and verbal, with the ability to explain complex technical concepts to non-technical stakeholders. Nice to Have : Experience with reinforcement learning or few-shot learning in the context of language models. Contributions to open-source projects or publications in top-tier AI/ML conferences (e.g., NeurIPS, ACL, ICML). What We Offer : Competitive salary and benefits package Flexible work schedule with remote work options Opportunity to work on cutting-edge AI technology with a passionate team A collaborative and inclusive work culture focused on innovation

Company Description Loyyal is a loyalty and payments innovation company that offers an Enterprise SaaS Suite powered by patented blockchain technology. We focus on disrupting the loyalty industry by delivering efficiency, security, and scalability at a low cost. Our platform is designed to reduce operational complexity and boost revenue for loyalty programs, driving customer engagement and loyalty in a competitive marketplace. About the Role We’re looking for a seasoned AI Engineer who thrives on solving complex challenges and building intelligent systems that scale. This role is ideal for someone passionate about deep learning, GenAI, and production-grade AI systems. You’ll work closely with our data, engineering, and product teams to design, build, and deploy advanced AI models across a variety of real-world use cases. As a Senior AI Engineer, you’ll play a key role in architecting, developing, and optimizing our AI systems—from fine-tuning large language models to building robust MLOps pipelines. This is an opportunity to be part of a high-impact team shaping next-generation AI experiences. Key Responsibilities Design, build, and deploy scalable AI models, with a focus on NLP, LLMs, and Generative AI use cases Fine-tune open-source or proprietary LLMs (e.g., LLaMA, Mistral, GPT-J) for domain-specific tasks Collaborate with product and engineering teams to integrate AI models into user-facing applications Develop MLOps pipelines using tools like MLflow, Kubeflow, or Vertex AI for model versioning, monitoring, and deployment Optimize inference performance, memory usage, and cost efficiency in production environments Apply prompt engineering, retrieval-augmented generation (RAG), and few-shot techniques where appropriate Conduct experiments, A/B testing, and evaluations to continuously improve model accuracy and reliability Stay up to date with the latest developments in AI/ML research, especially in LLM and GenAI domains Write clean, modular, and well-documented code and contribute to technical design reviews Mentor junior team members and collaborate in agile sprint cycles Requirements 6+ years of experience in machine learning or AI engineering 2+ years working with LLMs, Transformers, or Generative AI models Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow, Hugging Face Transformers) Experience deploying AI models in production (cloud-native or on-prem) Strong grasp of model fine-tuning, quantization, and serving at scale Familiarity with MLOps, including experiment tracking, CI/CD, and containerization (Docker, Kubernetes) Experience integrating AI with REST APIs, cloud services (AWS/GCP), and vector databases (e.g., Pinecone, Weaviate, FAISS) Understanding of ethical AI, data privacy, and fairness in model outcomes Strong debugging, problem-solving, and communication skills Experience working in agile teams with code review and version control (Git) Nice to Have Hands-on experience with Retrieval-Augmented Generation (RAG) pipelines Familiarity with OpenAI, Anthropic, or Cohere APIs and embedding models Knowledge of LangChain, LlamaIndex, or Haystack for AI application orchestration Experience with streaming data and real-time inference systems Understanding of multi-modal models (e.g., combining text, image, audio inputs) Prior experience in a startup, product-focused, or fast-paced R&D environment What We Offer Competitive compensation (base + performance-based bonuses or token equity) Fully remote and flexible work culture A front-row seat to build next-gen AI experiences in a high-growth environment Opportunity to shape AI strategy, tools, and infrastructure from the ground up Access to high-end GPU infrastructure and compute resources How to Apply Send your resume and a short cover letter highlighting: Your experience with LLMs, GenAI, and deployed AI systems Links to AI/ML projects, GitHub repos, or research (if public) Why you're interested in this role and how you envision contributing.

Sr. Data Scientist JD.docx Job Title: Senior Data Scientist | Machine Learning Engineer (MLE) Job Location: [Mohali / Pune] Experience: 4+ years Skill Sets Expertise in ML/DL, model lifecycle management, and MLOps (MLflow, Kubeflow) Proficiency in Python, TensorFlow, PyTorch, Scikit-learn, and Hugging Face models Strong experience in NLP, fine-tuning transformer models, and dataset preparation Hands-on with cloud platforms (AWS, GCP, Azure) and scalable ML deployment (Sagemaker, Vertex AI) Experience in containerization (Docker, Kubernetes) and CI/CD pipelines Knowledge of distributed computing (Spark, Ray), vector databases (FAISS, Milvus), and model optimization (quantization, pruning) Familiarity with model evaluation, hyperparameter tuning, and model monitoring for drift detection Roles & Responsibilities Design and implement end-to-end ML pipelines from data ingestion to production Develop, fine-tune, and optimize ML models, ensuring high performance and scalability Compare and evaluate models using key metrics (F1-score, AUC-ROC, BLEU etc) Automate model retraining, monitoring, and drift detection Collaborate with engineering teams for seamless ML integration Mentor junior team members and enforce best practices

AI Research Intern – AryaXAI AI Alignment Labs Location: Remote / Mumbai, India Commitment: Full-time internship (6 months; potential extension or full-time offer) Start Date: Rolling About AryaXAI AI Alignment Labs AryaXAI AI Alignment Labs, based out of Mumbai, India and Paris, France, is the alignment and explainability division of AryaXAI.com , part of Aurionpro Solutions. We work on AI interpretability and trustworthiness for mission-critical sectors. Our open-source initiatives include the xai_evals benchmarking suite and the DLBacktrace explainability framework, both designed to make AI more transparent, reliable, and aligned with human values. What You’ll Do Collaborate closely with our research and engineering teams on one of the areas: Library Development: Architect and enhance open-source Python tooling for alignment, explainability, uncertainty quantification, robustness, and machine unlearning. Model Benchmarking: Conduct rigorous evaluations of LLMs and deep networks under domain shifts, adversarial conditions, and regulatory constraints. Explainability & Trust: Design and implement XAI techniques (LRP, SHAP, Grad-CAM, Backtrace) across text, image, and tabular modalities. Mechanistic Interpretability: Probe internal model representations and circuits—using activation patching, feature visualization, and related methods—to diagnose failure modes and emergent behaviors. Uncertainty & Risk: Develop, implement, and benchmark uncertainty estimation methods (Bayesian approaches, ensembles, test-time augmentation) alongside robustness metrics for foundation models. Research Contributions: Author and maintain experiment code, run systematic studies, and co-author whitepapers or conference submissions. General Required Qualifications Strong Python expertise: writing clean, modular, and testable code. Theoretical foundations: deep understanding of machine learning and deep learning principles with hands-on experience with PyTorch. Transformer architectures & fundamentals: comprehensive knowledge of attention mechanisms, positional encodings, tokenization and training objectives in BERT, GPT, LLaMA, T5, MOE, Mamba, etc. Version control & CI/CD: Git workflows, packaging, documentation, and collaborative development practices. Collaborative mindset: excellent communication, peer code reviews, and agile teamwork. Preferred Domain Expertise (Any one of these is good) : Explainability: applied experience with XAI methods such as SHAP, LIME, IG, LRP, DL-Bactrace or Grad-CAM. Mechanistic interpretability: familiarity with circuit analysis, activation patching, and feature visualization for neural network introspection. Uncertainty estimation: hands-on with Bayesian techniques, ensembles, or test-time augmentation. Quantization & pruning: applying model compression to optimize size, latency, and memory footprint. LLM Alignment techniques: crafting and evaluating few-shot, zero-shot, and chain-of-thought prompts; experience with RLHF workflows, reward modeling, and human-in-the-loop fine-tuning. Post-training adaptation & fine-tuning: practical work with full-model fine-tuning and parameter-efficient methods (LoRA, adapters), instruction tuning, knowledge distillation, and domain-specialization. Additional Experience (Nice-to-Have) Publications: contributions to CVPR, ICLR, ICML, KDD, WWW, WACV, NeurIPS, ACL, NAACL, EMNLP, IJCAI or equivalent research experience. Open-source contributions: prior work on AI/ML libraries or tooling. Domain exposure: risk-sensitive applications in finance, healthcare, or similar fields. Performance optimization: familiarity with large-scale training infrastructures. What We Offer Real-world impact: address high-stakes AI challenges in regulated industries. Compute resources: access to GPUs, cloud credits, and proprietary models. Competitive stipend: with potential for full-time conversion. Authorship opportunities: co-authorship on papers, technical reports, and conference submissions.

CTO&/CTO&/2025/2685598 Job Title: Vision X Principal with Gen / AI architect. Location: Chennai and Noida Exp :- 15 to 20+ Primary skills :- Vision AI Solution, Nvidia, Computer Vision, Media, Open Stack. Job Type: Full-time Band : E5 Key Responsibilities Define and lead the end-to-end technical architecture for vision-based AI systems across edge and cloud. Design and optimize large-scale video analytics pipelines using NVIDIA DeepStream, TensorRT, and Triton Inference Server. Architect distributed AI systems, including model training, deployment, inferencing, monitoring, and continuous learning. Collaborate with product, research, and engineering teams to translate business requirements into scalable AI solutions. Lead efforts in model optimization (quantization, pruning, distillation) for real-time performance on devices like Jetson Orin/Xavier. Drive the integration of multi-modal AI (vision + language, 3D, audio) where applicable. Guide platform choices (e.g., edge AI vs cloud AI trade-offs), ensuring cost-performance balance. Mentor senior engineers and promote best practices in MLOps, system reliability, and AI observability. Stay current with emerging technologies (e.g., NeRF, Diffusion Models, Vision Transformers, synthetic data). Contribute to internal innovation strategy, including IP generation, publications, and external presentations. ________________________________________ 🛠️ Required Technical Skills Deep expertise in computer vision, deep learning, and multi-modal AI. Proven hands-on experience with: NVIDIA Jetson, DeepStream SDK, TensorRT, Triton Inference Server TAO Toolkit, Isaac SDK, CUDA, cuDNN Strong in PyTorch, TensorFlow, OpenCV, GStreamer, and GPU-accelerated pipelines. Experience deploying vision AI models at large scale (e.g., 1000+ cameras/devices or multi-GPU clusters). Skilled in cloud-native ML infrastructure: Docker, Kubernetes, CI/CD, MLflow, Seldon, Airflow Proficiency in Python, C++, CUDA (or PyCUDA), and scripting. Familiar with 3D vision, synthetic data pipelines, and generative models (e.g., SAM, NeRF, Diffusion). Experience in multi modal (LVM/VLM), SLMs, small LVM/ VLM, Time series Gen AI models, Agentic AI, LLMOps/Edge LLMOps, Guardrails, Security in Gen AI, YOLO/Vision Transformers ________________________________________ 🤝 Soft Skills & Leadership 10+ years in AI/ML/Computer Vision, with 8+ years in technical leadership or architect roles Strong leadership skills with experience mentoring technical teams and driving innovation. Excellent communicator with the ability to engage stakeholders across engineering, product, and business. Strategic thinker with a practical mindset—able to balance innovation with production-readiness. Experience interfacing with enterprise customers, researchers, and hardware partners. ________________________________________ 🧩 Preferred Qualifications MS or PhD in Computer Vision, Machine Learning, Robotics, or a related technical field ( Added Advantage ) Experience with NVIDIA Omniverse, Clara, or MONAI for healthcare or simulation environments. Experience in domains like smart cities, robotics, retail analytics, or medical imaging. Contributions to open-source projects or technical publications. Certifications: NVIDIA Jetson Developer, AWS/GCP AI/ML Certifications. ________________________________________

Company Description 👋🏼 We're Nagarro. We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale across all devices and digital mediums, and our people exist everywhere in the world (17500+ experts across 39 countries, to be exact). Our work culture is dynamic and non-hierarchical. We're looking for great new colleagues. That's where you come in! Job Description REQUIREMENTS: Total experience: 10+ years. Strong experience with LLMs (LLaMA, DeepSeek, etc.) and understanding of RAG pipelines. Hands on experience in Python, Linux, and Shell scripting Experience with OpenCV, PyTorch, YOLO, or TensorFlow frameworks Familiarity with LLM inference engines like Ollama, vLLM, llama.cpp Solid knowledge of model conversion and deployment. Experience working on AI Agents, LangChain, and retrieval-augmented generation (RAG) Hands-on experience with Docker, Docker Compose, and integration into DevOps pipelines Understanding of embedded platforms (Jetson, NXP, Qualcomm) and Yocto builds. Experience in model optimization techniques (quantization, pruning, etc.) Good grasp of CUDA kernels and GPU computing for acceleration Excellent communication skills and the ability to collaborate effectively with cross-functional teams. RESPONSIBILITIES: Understanding functional requirements thoroughly and analyzing the client’s needs in the context of the project Envisioning the overall solution for defined functional and non-functional requirements, and being able to define technologies, patterns and frameworks to realize it Determining and implementing design methodologies and tool sets Enabling application development by coordinating requirements, schedules, and activities. Being able to lead/support UAT and production roll outs Creating, understanding and validating WBS and estimated effort for given module/task, and being able to justify it Addressing issues promptly, responding positively to setbacks and challenges with a mindset of continuous improvement Giving constructive feedback to the team members and setting clear expectations. Qualifications Bachelor’s or master’s degree in computer science, Information Technology, or a related field.

Opportunity Description: Senior AI/ML Computer Vision Engineer - Tech Lead At Jinn Labs, we’re reimagining how retail security works—using cutting-edge Vision AI to stop theft before it happens. Our mission? Solve the $100B shrinkage problem in retail with AI-native agents that detect, understand, and act—starting with the most expensive and overlooked issue: employee theft. Who should consider this opportunity? We’re looking for candidates who geek out on every stage of the computer-vision pipeline: whether you’re wrangling raw pixels into meaningful features, architecting and running experiments to validate new models, or building bullet-proof data-cleaning pipelines that tame messy, real-world footage. You’ll apply applied statistics at scale—A/B testing architectures, tuning hyperparameters against principled metrics, and interpreting confidence intervals—to drive measurable gains in detection and re-identification accuracy. If you love transforming noise into insight and seeing your algorithms deployed in live retail settings—where each increment in performance directly reduces shrinkage and elevates the customer experience n this role, you’ll also take the helm in shaping the vision and design of our solutions: defining the core problem statements, mapping out end-to-end data flows, and translating business goals into actionable technical roadmaps. You’ll guide cross-functional teams on experiment design, set success criteria, and prioritize features based on impact and feasibility. Your ability to frame complex challenges, sketch out robust evaluation plans, and push stakeholders toward clear, data-driven decisions will be critical to fast-tracking our path from prototype to production. What We’re Building: Our real-time system integrates seamlessly with existing cameras and POS setups in convenience stores, gas stations, and small-format retail. No human monitoring. Just actionable insights, automated responses, and a measurable impact on the bottom line. Why Jinn? We’re backed by the Allen Institute for AI (AI2) and have a team with over 100 years of experience across computer vision, ML, and production engineering. Our founders and advisors have driven growth at Amazon, Microsoft, LinkedIn, Rippling, and more. We’ve already helped our first customers unlock major ROI—and we’re just getting started. Why Now? We’re well-funded, on the path to raise our seed round, and scaling fast. Joining us now means shaping the core product, influencing the roadmap, and building a system that will be in thousands of stores in the next year. What You’ll Do: Build CV models that run reliably in the wild—across edge devices and cloud pipelines Work with a world-class team obsessed with scientific rigor and product impact Tackle challenges across detection, tracking, multi-modal fusion, and real-time inference Own your work, end-to-end—from research to production We’d Love to Meet You If: You’ve shipped ML/CV models in production (bonus if it’s on the edge) You’re excited by real-world messiness and solving hard technical problems You want your work to matter—measurably—to customers, not just metrics You’re ready to grow fast with a startup that’s moving even faster (More details below) Role Description The Senior Computer Vision Engineer will be responsible for developing and implementing advanced computer vision algorithms, pattern recognition models, on cloud and on the edge computing to improve retail security and operational efficiency. Key Responsibilities Lead the design, development, training, and deployment of state-of-the-art computer vision and machine learning models for real-time applications in retail environments. Develop and optimize algorithms for tasks such as: Real-time object detection, tracking (people, products), and re-identification. Action recognition and behavior analysis for identifying theft-related activities and operational inefficiencies. Anomaly detection in video feeds and integrated POS data. Architect and implement robust MLOps pipelines for model versioning, training, deployment, monitoring, and continuous improvement on both edge devices and cloud platforms. Optimize model performance (latency, throughput, accuracy) for deployment on edge devices with limited computational resources Collaborate closely with software engineers to integrate CV models into the broader product architecture and ensure seamless data flow from existing camera infrastructure and POS systems. Work with large-scale, real-world video datasets, including developing strategies for data acquisition, augmentation, annotation, and quality control. Mentor junior engineers and contribute to a culture of technical excellence and innovation. Contribute to system-level design and decision-making regarding our vision pipeline and overall AI capabilities. Required qualifications 5+ years of hands-on experience in developing and deploying computer vision and machine learning models in production environments. Deep understanding and practical experience with PyTorch, NumPy; strong proficiency with CUDA for GPU acceleration. Ability to work with different model architectures including transformers, CNN and LSTM. Demonstrable experience processing and analyzing multiple concurrent video feeds in real-time, with deployments on edge devices and/or cloud platforms. Expert programming skills in Python; proficiency in C++ is a strong plus for performance-critical components. Strong theoretical understanding and practical application of Computer Vision techniques (e.g., image processing, feature extraction, multi-view geometry) and Pattern Recognition. Extensive experience in the end-to-end lifecycle of AI vision models, including data collection, labeling and annotations, training from scratch, fine-tuning, evaluation, and deployment. Strong analytical and problem-solving skills Preferred qualifications Master's or Ph.D. in Computer Science, with a specialization in Computer Vision or Machine Learning. Knowledge of at least one cloud provider stack (GCP preferred) Experience with quantization, pruning, knowledge distillation, TensorRT, ONNX Experience with edge computing platforms and SDKs (e.g., NVIDIA Jetson, DeepStream SDK, Hailo, Raspberry Pi). * Understanding of challenges specific to retail environments (e.g., varying lighting conditions, occlusions, camera angles, low-resolution footage) Send your cover letter and resume to Hr@jinnlabs.ai for consideration.

About Us Zycus is a pioneer in Cognitive Procurement software and has been a trusted partner of choice for large global enterprises for two decades. Zycus has been consistently recognized by Gartner, Forrester, and other analysts for its Source to Pay integrated suite. Zycus powers its S2P software with the revolutionary Merlin AI Suite. Merlin AI takes over the tactical tasks and empowers procurement and AP officers to focus on strategic projects; offers data-driven actionable insights for quicker and smarter decisions, and its conversational AI offers a B2C type user-experience to the end-users. Zycus helps enterprises drive real savings, reduce risks, and boost compliance, and its seamless, intuitive, and easy-to-use user interface ensures high adoption and value across the organization. Start your #CognitiveProcurement journey with us, as you are #MeantforMore We Are An Equal Opportunity Employer: Zycus is committed to providing equal opportunities in employment and creating an inclusive work environment. We do not discriminate against applicants on the basis of race, color, religion, gender, sexual orientation, national origin, age, disability, or any other legally protected characteristic. All hiring decisions will be based solely on qualifications, skills, and experience relevant to the job requirements. Job Description We seek an innovative AI Engineer (Experience 2 - 5 yrs) to join our team and lead the development of scalable solutions using open-source technologies, LLM APIs , and advanced AI techniques. The ideal candidate will excel in designing RAG, Graph RAG, Agent Systems with function calling , and fine-tuning/customizing LLMs. Proficiency in hosting open-source models (e.g., Llama 2, Mistral) and integrating APIs (OpenAI, Anthropic, etc.) is critical, along with experience in Python frameworks like Fast API/Flask for production-grade deployments. Key Responsibilities: Architect, build, and optimize AI solutions using open-source models (e.g., Hugging Face, Ollama) and third-party LLM APIs. Design and implement advanced techniques including RAG, GraphRAG, Agent Systems with orchestration/function calling, and fine-tuning/prompt-tuning of LLMs. Deploy and manage self-hosted open-source models (e.g., via vLLM, TensorRT-LLM) with scalable APIs. Collaborate with teams to integrate AI/ML solutions into production systems using FastAPI, Flask, or similar frameworks. Develop automation pipelines for data retrieval, preprocessing, and model evaluation, ensuring alignment with business use cases. Stay ahead of AI trends (e.g., open-source LLM advancements, cost-efficient scaling) and drive strategic adoption. Ensure robust monitoring, testing, and documentation of systems for reliability and reproducibility. Five Reasons Why You Should Join Zycus Cloud Product Company: We are a Cloud SaaS Company, and our products are created by using the latest technologies like ML and AI. Our UI is in Angular JS, and we are developing our mobile apps using React. A Market Leader: Zycus is recognized by Gartner (world’s leading market research analyst) as a Leader in Procurement Software Suites. Move between Roles: We believe that change leads to growth and therefore we allow our employees to shift careers and move to different roles and functions within the organization Get a Global Exposure: You get to work and deal with our global customers. Create an Impact: Zycus gives you the environment to create an impact on the product and transform your ideas into reality. Even our junior engineers get the opportunity to work on different product features. About Us Zycus is a pioneer in Cognitive Procurement software and has been a trusted partner of choice for large global enterprises for two decades. Zycus has been consistently recognized by Gartner, Forrester, and other analysts for its Source to Pay integrated suite. Zycus powers its S2P software with the revolutionary Merlin AI Suite. Merlin AI takes over the tactical tasks and empowers procurement and AP officers to focus on strategic projects; offers data-driven actionable insights for quicker and smarter decisions, and its conversational AI offers a B2C type user-experience to the end-users. Zycus helps enterprises drive real savings, reduce risks, and boost compliance, and its seamless, intuitive, and easy-to-use user interface ensures high adoption and value across the organization. Start your #CognitiveProcurement journey with us, as you are #MeantforMore Job Requirement Experience & Qualifications: Bachelor’s/master’s in computer science, AI, or related field. Expertise in Python and backend frameworks like FastAPI/Flask. Hands-on experience with RAG architectures, Agent Systems (function calling/tool use), GraphRAG, or similar LLM-driven workflows. Ability to fine-tune LLMs (LoRA, QLoRA) and host/deploy open-source models (Llama 2, Mistral, etc.). Proficiency with LLM APIs (OpenAI, Anthropic, Groq) and vector databases (Pinecone, Qdrant, pgvector). Familiarity with NLP/ML frameworks (PyTorch, Transformers, LangChain, LlamaIndex) and cloud platforms (AWS, Azure, GCP). Skilled in building scalable APIs and microservices for AI applications. Preferred Qualifications: Experience optimizing inference for open-source models (quantization, distillation). Familiarity with multi-agent systems, chain-of-thought prompting, or LLM eval frameworks. Knowledge of distributed training, GPU optimization, and MLOps (MLflow, Kubeflow). Contributions to open-source AI/ML projects.

About Us Zycus is a pioneer in Cognitive Procurement software and has been a trusted partner of choice for large global enterprises for two decades. Zycus has been consistently recognized by Gartner, Forrester, and other analysts for its Source to Pay integrated suite. Zycus powers its S2P software with the revolutionary Merlin AI Suite. Merlin AI takes over the tactical tasks and empowers procurement and AP officers to focus on strategic projects; offers data-driven actionable insights for quicker and smarter decisions, and its conversational AI offers a B2C type user-experience to the end-users. Zycus helps enterprises drive real savings, reduce risks, and boost compliance, and its seamless, intuitive, and easy-to-use user interface ensures high adoption and value across the organization. Start your #CognitiveProcurement journey with us, as you are #MeantforMore We Are An Equal Opportunity Employer: Zycus is committed to providing equal opportunities in employment and creating an inclusive work environment. We do not discriminate against applicants on the basis of race, color, religion, gender, sexual orientation, national origin, age, disability, or any other legally protected characteristic. All hiring decisions will be based solely on qualifications, skills, and experience relevant to the job requirements. Job Description We seek an innovative AI Engineer (Experience 2 - 5 yrs) to join our team and lead the development of scalable solutions using open-source technologies, LLM APIs , and advanced AI techniques. The ideal candidate will excel in designing RAG, Graph RAG, Agent Systems with function calling , and fine-tuning/customizing LLMs. Proficiency in hosting open-source models (e.g., Llama 2, Mistral) and integrating APIs (OpenAI, Anthropic, etc.) is critical, along with experience in Python frameworks like Fast API/Flask for production-grade deployments. Key Responsibilities: Architect, build, and optimize AI solutions using open-source models (e.g., Hugging Face, Ollama) and third-party LLM APIs. Design and implement advanced techniques including RAG, GraphRAG, Agent Systems with orchestration/function calling, and fine-tuning/prompt-tuning of LLMs. Deploy and manage self-hosted open-source models (e.g., via vLLM, TensorRT-LLM) with scalable APIs. Collaborate with teams to integrate AI/ML solutions into production systems using FastAPI, Flask, or similar frameworks. Develop automation pipelines for data retrieval, preprocessing, and model evaluation, ensuring alignment with business use cases. Stay ahead of AI trends (e.g., open-source LLM advancements, cost-efficient scaling) and drive strategic adoption. Ensure robust monitoring, testing, and documentation of systems for reliability and reproducibility. Five Reasons Why You Should Join Zycus Cloud Product Company: We are a Cloud SaaS Company, and our products are created by using the latest technologies like ML and AI. Our UI is in Angular JS, and we are developing our mobile apps using React. A Market Leader: Zycus is recognized by Gartner (world’s leading market research analyst) as a Leader in Procurement Software Suites. Move between Roles: We believe that change leads to growth and therefore we allow our employees to shift careers and move to different roles and functions within the organization Get a Global Exposure: You get to work and deal with our global customers. Create an Impact: Zycus gives you the environment to create an impact on the product and transform your ideas into reality. Even our junior engineers get the opportunity to work on different product features. About Us Zycus is a pioneer in Cognitive Procurement software and has been a trusted partner of choice for large global enterprises for two decades. Zycus has been consistently recognized by Gartner, Forrester, and other analysts for its Source to Pay integrated suite. Zycus powers its S2P software with the revolutionary Merlin AI Suite. Merlin AI takes over the tactical tasks and empowers procurement and AP officers to focus on strategic projects; offers data-driven actionable insights for quicker and smarter decisions, and its conversational AI offers a B2C type user-experience to the end-users. Zycus helps enterprises drive real savings, reduce risks, and boost compliance, and its seamless, intuitive, and easy-to-use user interface ensures high adoption and value across the organization. Start your #CognitiveProcurement journey with us, as you are #MeantforMore Job Requirement Experience & Qualifications: Bachelor’s/master’s in computer science, AI, or related field. Expertise in Python and backend frameworks like FastAPI/Flask. Hands-on experience with RAG architectures, Agent Systems (function calling/tool use), GraphRAG, or similar LLM-driven workflows. Ability to fine-tune LLMs (LoRA, QLoRA) and host/deploy open-source models (Llama 2, Mistral, etc.). Proficiency with LLM APIs (OpenAI, Anthropic, Groq) and vector databases (Pinecone, Qdrant, pgvector). Familiarity with NLP/ML frameworks (PyTorch, Transformers, LangChain, LlamaIndex) and cloud platforms (AWS, Azure, GCP). Skilled in building scalable APIs and microservices for AI applications. Preferred Qualifications: Experience optimizing inference for open-source models (quantization, distillation). Familiarity with multi-agent systems, chain-of-thought prompting, or LLM eval frameworks. Knowledge of distributed training, GPU optimization, and MLOps (MLflow, Kubeflow). Contributions to open-source AI/ML projects.

Hiring: Senior Data Scientist | Machine Learning Engineer (MLE) Location : Mohali / Pune Experience : 4+ Years Salary : ₹25 LPA – ₹30 LPA Apply at : info@fitb.in Required Skill Sets Expertise in ML/DL , model lifecycle management, and MLOps tools (MLflow, Kubeflow) Proficiency in Python , TensorFlow , PyTorch , Scikit-learn , and Hugging Face Strong background in NLP , including fine-tuning transformer models Hands-on experience with AWS , GCP , or Azure , and deployment tools like SageMaker , Vertex AI Knowledge of Docker , Kubernetes , and CI/CD pipelines Familiarity with distributed computing (Spark, Ray) and vector databases (FAISS, Milvus) Experience with model optimization (quantization, pruning), hyperparameter tuning , and drift detection Roles & Responsibilities Build and maintain end-to-end ML pipelines from data ingestion to deployment Develop, fine-tune, and scale ML models for real-world applications Evaluate models using metrics like F1-score , AUC-ROC , BLEU , etc. Automate retraining, monitoring , and drift detection processes Collaborate with cross-functional teams for seamless ML integration Mentor junior team members and enforce best practices Perks & Benefits Food allowance provided Cab facility available Night shift allowance (NCA) Graduates only may apply 5-day working If you meet the above criteria and are passionate about building impactful ML systems, send your resume to info@fitb.in Job Types: Full-time, Permanent, Fresher Pay: ₹2,500,000.00 - ₹3,000,000.00 per year Benefits: Flexible schedule Food provided Health insurance Life insurance Paid sick time Paid time off Provident Fund Schedule: Evening shift Fixed shift Monday to Friday Night shift Rotational shift US shift Supplemental Pay: Performance bonus Shift allowance Yearly bonus Work Location: In person

Job Description: As a Senior Machine Learning Engineer , you will be responsible for designing, developing, and deploying cutting-edge models for end-to-end content generation , including AI-driven image/video generation, lipsyncing, and multimodal AI systems . You will work on the latest advancements in deep generative modeling to create highly realistic and controllable AI-generated media. Responsibilities: Research & Develop : Design and implement state-of-the-art generative models , including Diffusion Models, 3D VAEs and GANs for AI-powered media synthesis . End-to-End Content Generation : Build and optimize AI pipelines for high-fidelity image/video generation and lipsyncing using diffusion and autoencoder models. Speech & Video Synchronization : Develop advanced lipsyncing and multimodal generation models that integrate speech, video, and facial animation for hyper-realistic AI-driven content. Real-Time AI Systems : Implement and optimize models for real-time content generation and interactive AI applications using efficient model architectures and acceleration techniques . Scaling & Production Deployment : Work closely with software engineers to deploy models efficiently on cloud-based architectures (AWS, GCP, or Azure) . Collaboration & Research : Stay ahead of the latest trends in deep generative models, diffusion models, and transformer-based vision systems to enhance AI-generated content quality. Experimentation & Validation : Design and conduct experiments to evaluate model performance, improve fidelity, realism, and computational efficiency , and refine model architectures. Code Quality & Best Practices : Participate in code reviews, improve model efficiency, and document research findings to enhance team knowledge-sharing and product development . Qualifications: Bachelor's or Master’s degree in Computer Science, Machine Learning, or a related field. 3+ years of experience working with deep generative models , including Diffusion Models, 3D VAEs, GANs and autoregressive models . Strong proficiency in Python and deep learning frameworks such as PyTorch. Expertise in multi-modal AI, text-to-image, and image-to-video generation , audio to lipsync Strong understanding of machine learning principles and statistical methods. Good to have experience in real-time inference optimization, cloud deployment, and distributed training . Strong problem-solving abilities and a research-oriented mindset to stay updated with the latest AI advancements. Familiarity with generative adversarial techniques, reinforcement learning for generative models, and large-scale AI model training . Preferred Qualifications: Experience with transformers and vision-language models (e.g., CLIP, BLIP, GPT-4V). Background in text-to-video generation, lipsync generation and real-time synthetic media applications . Experience in cloud-based AI pipelines (AWS, Google Cloud, or Azure) and model compression techniques (quantization, pruning, distillation) . Contributions to open-source projects or published research in AI-generated content, speech synthesis, or video synthesis .

Key Responsibilities Design and develop advanced generative models including Diffusion Models, GANs, 3D VAEs, and autoregressive models for AI-powered media synthesis. Build and optimize end-to-end content generation pipelines for high-fidelity image, video, and lip-sync generation. Develop multimodal AI systems that integrate speech, video, and facial animation for hyper-realistic content output. Implement and fine-tune models for real-time performance, using techniques such as model quantization, pruning, and distillation. Collaborate with engineering teams to deploy AI models on scalable cloud platforms (AWS, GCP, Azure). Conduct rigorous experimentation to improve model accuracy, realism, and computational efficiency. Stay updated with the latest research in deep generative models, transformer-based vision systems, and multimodal AI. Participate in code reviews, maintain high standards of code quality, and document key findings to support internal knowledge sharing. Requirements Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field. Minimum 3 years of hands-on experience working with deep generative models (Diffusion Models, GANs, VAEs). Strong proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow. Proven experience in text-to-image, image-to-video, and audio-to-lip-sync model development. Solid understanding of machine learning principles, statistical modeling, and neural network architectures. Familiarity with real-time inference optimization and deployment on cloud environments (AWS, GCP, Azure). Experience working with multimodal architectures and transformer-based models like CLIP, BLIP, or GPT-4V. Contributions to open-source projects or peer-reviewed publications in generative AI, speech synthesis, or video generation are a plus. Key Skills PyTorch AWS Google Cloud Platform (GCP) Azure

Company Description Echoleads.ai leverages AI-powered sales agents to engage, qualify, and convert leads through real-time voice conversations. Our voice bots act as scalable sales representatives, making thousands of smart, human-like calls daily to follow up instantly, ask the right questions, and book appointments effortlessly. Echoleads integrates seamlessly with lead sources like Meta Ads, Google Ads, and CRMs, ensuring leads are never missed. Serving modern sales and marketing teams across various industries, our AI agents proficiently handle outreach, lead qualification, and appointment setting. About the Role: We are seeking a highly experienced Voice AI /ML Engineer to lead the design and deployment of real-time voice intelligence systems. This role focuses on ASR, TTS, speaker diarization, wake word detection, and building production-grade modular audio processing pipelines to power next-generation contact center solutions, intelligent voice agents, and telecom-grade audio systems. You will work at the intersection of deep learning, streaming infrastructure, and speech/NLP technology, creating scalable, low-latency systems across diverse audio formats and real-world applications. Key Responsibilities: Voice & Audio Intelligence: Build, fine-tune, and deploy ASR models (e.g., Whisper, wav2vec2.0, Conformer) for real-time transcription. Develop and finetune high-quality TTS systems using VITS, Tacotron, FastSpeech for lifelike voice generation and cloning. Implement speaker diarization for segmenting and identifying speakers in multi-party conversations using embeddings (x-vectors/d-vectors) and clustering (AHC, VBx, spectral clustering). Design robust wake word detection models with ultra-low latency and high accuracy in noisy conditions. Real-Time Audio Streaming & Voice Agent Infrastructure: Architect bi-directional real-time audio streaming pipelines using WebSocket, gRPC, Twilio Media Streams, or WebRTC. Integrate voice AI models into live voice agent solutions, IVR automation, and AI contact center platforms. Optimize for latency, concurrency, and continuous audio streaming with context buffering and voice activity detection (VAD). Build scalable microservices to process, decode, encode, and stream audio across common codecs (e.g., PCM, Opus, μ-law, AAC, MP3) and containers (e.g., WAV, MP4). Deep Learning & NLP Architecture: Utilize transformers, encoder-decoder models, GANs, VAEs, and diffusion models, for speech and language tasks. Implement end-to-end pipelines including text normalization, G2P mapping, NLP intent extraction, and emotion/prosody control. Fine-tune pre-trained language models for integration with voice-based user interfaces. Modular System Development: Build reusable, plug-and-play modules for ASR, TTS, diarization, codecs, streaming inference, and data augmentation. Design APIs and interfaces for orchestrating voice tasks across multi-stage pipelines with format conversions and buffering. Develop performance benchmarks and optimize for CPU/GPU, memory footprint, and real-time constraints. Engineering & Deployment: Writing robust, modular, and efficient Python code Experience with Docker, Kubernetes, cloud deployment (AWS, Azure, GCP) Optimize models for real-time inference using ONNX, TorchScript, and CUDA, including quantization, context-aware inference, model caching. On device voice model deployment.

Hyderabad, Telangana, India Job Type Full Time About the Role About the Role We are looking for a hands-on and technically proficient Embedded Software Team Lead to drive the development of intelligent edge systems that combine embedded firmware, machine learning inference, and hardware acceleration. This role is perfect for someone who thrives at the intersection of real-time firmware design, AI model deployment, and hardware-software co-optimization. You will lead a team delivering modular, scalable, and efficient firmware pipelines that run quantized ML models on accelerators like Hailo, Coral, Torrent (BlackHole), Kendryte, and other emerging chipsets. Your focus will include model runtime integration, low-latency sensor processing, OTA-ready firmware stacks, and CI/CD pipelines for embedded products at scale Requirements Key Responsibilities Technical Leadership & Planning Own the firmware lifecycle across multiple AI-based embedded product lines. Define system and software architecture in collaboration with hardware, ML, and cloud teams. Lead sprint planning, code reviews, performance debugging, and mentor junior engineers. ️ ML Model Deployment & Runtime Integration Collaborate with ML engineers to port, quantize, and deploy models using TFLite , ONNX , or HailoRT . Build runtime pipelines that connect model inference with real-time sensor data (vision, IMU, acoustic). Optimize memory and compute flows for edge model execution under power/bandwidth constraints. Firmware Development & Validation Build production-grade embedded stacks using RTOS (FreeRTOS/Zephyr) or embedded Linux . Implement secure bootloaders, OTA update mechanisms, and encrypted firmware interfaces. Interface with a variety of peripherals including cameras, IMUs, analog sensors, and radios (BLE/Wi-Fi/LoRa). ️ CI/CD, DevOps & Tooling for Embedded Set up and manage CI/CD pipelines for firmware builds, static analysis, and validation. Integrate Docker-based toolchains, hardware-in-loop (HIL) testing setups, and simulators/emulators. Ensure codebase quality, maintainability, and test coverage across the embedded stack. Required Qualifications ‍ Education: BE/B.Tech/M.Tech in Embedded Systems, Electronics, Computer Engineering, or related fields. Experience: Minimum 4+ years of embedded systems experience. Minimum 2 years in a technical lead or architect role. Hands-on experience in ML model runtime optimization and embedded system integration. Technical Skills Required Embedded Development & Tools Expert-level C/C++ , hands-on with RTOS and Yocto-based Linux . Proficient with toolchains like GCC/Clang, OpenOCD, JTAG/SWD, Logic Analyzers. Familiarity with OTA , bootloaders , and memory management (heap/stack analysis, linker scripts). ML Model Integration Proficiency in TFLite , ONNX Runtime , HailoRT , or EdgeTPU runtimes . Experience with model conversion, quantization (INT8, FP16), runtime optimization. Ability to read/modify model graphs and connect to inference APIs. Connectivity & Peripherals Working knowledge of BLE, Wi-Fi, LoRa, RS485 , USB, and CAN protocols. Integration of camera modules , MIPI CSI , IMUs , and custom analog sensors . ️ DevOps for Embedded Hands-on with GitLab/GitHub CI, Docker, and containerized embedded builds. Build system expertise: CMake , Make , Bazel , or Yocto preferred. Experience in automated firmware testing (HIL, unit, integration). Preferred (Bonus) Skills Familiarity with machine vision pipelines , ISP tuning , or video/audio codec integration . Prior work on battery-operated devices , energy-aware scheduling , or deep sleep optimization . Contributions to embedded ML open-source projects or model deployment tools. Why Join Us? At EURTH TECHTRONICS PVT LTD , we go beyond firmware—we’re designing and deploying embedded intelligence on every device, from industrial gateways to smart consumer wearables. Build and lead teams working on cutting-edge real-time firmware + ML integration . Work on full-stack embedded ML systems using the latest AI accelerators and embedded chipsets . Drive product-ready, scalable software platforms that power IoT, defense, medical , and consumer electronics . How to Apply Send your updated resume + GitHub/portfolio links to: jobs@eurthtech.com About the Company About EURTH TECHTRONICS PVT LTD EURTH TECHTRONICS PVT LTD is a cutting-edge Electronics Product Design and Engineering firm specializing in embedded systems, IoT solutions, and high-performance hardware development. We provide end-to-end product development services—from PCB design, firmware development, and system architecture to manufacturing and scalable deployment. With deep expertise in embedded software, signal processing, AI-driven edge computing, RF communication, and ultra-low-power design, we build next-generation industrial automation, consumer electronics, and smart infrastructure solutions. Our Core Capabilities Embedded Systems & Firmware Engineering – Architecting robust, real-time embedded solutions with RTOS, Linux, and MCU/SoC-based firmware. IoT & Wireless Technologies – Developing LoRa, BLE, Wi-Fi, UWB, and 5G-based connected solutions for industrial and smart city applications. Hardware & PCB Design – High-performance PCB layout, signal integrity optimization, and design for manufacturing (DFM/DFA). Product Prototyping & Manufacturing – Accelerating concept-to-market with rapid prototyping, design validation, and scalable production. AI & Edge Computing – Implementing real-time AI/ML on embedded devices for predictive analytics, automation, and security. Security & Cryptography – Integrating post-quantum cryptography, secure boot, and encrypted firmware updates. Our Industry Impact ✅ IoT & Smart Devices – Powering the next wave of connected solutions for industrial automation, logistics, and smart infrastructure. ✅ Medical & Wearable Tech – Designing low-power biomedical devices with precision sensor fusion and embedded intelligence. ✅ Automotive & Industrial Automation – Developing AI-enhanced control systems, predictive maintenance tools, and real-time monitoring solutions. ✅ Scalable Enterprise & B2B Solutions – Delivering custom embedded hardware and software tailored to OEMs, manufacturers, and system integrators. Our Vision We are committed to advancing technology and innovation in embedded product design. With a focus on scalability, security, and efficiency, we empower businesses with intelligent, connected, and future-ready solutions. We currently cater to B2B markets, offering customized embedded development services, with a roadmap to expand into direct-to-consumer (B2C) solutions.

Hyderabad, Telangana, India Job Type Full Time About the Role About the Role We are seeking a passionate and skilled Embedded ML Engineer to work on cutting-edge ML inference pipelines for low-power, real-time embedded platforms. You will help design and deploy highly efficient ML models on custom hardware accelerators like Hailo, Coral (Edge TPU), Kendryte K210, and Torrent/BlackHole in real-world IoT systems. This role combines model optimization, embedded firmware development, and toolchain management. You will be responsible for translating large ML models into efficient quantized versions, benchmarking them on custom hardware, and integrating them with embedded firmware pipelines that interact with real-world sensors and peripherals. Requirements Key Responsibilities ML Model Optimization & Conversion Convert, quantize, and compile models built in TensorFlow, PyTorch , or ONNX to hardware-specific formats. Work with compilers and deployment frameworks like TFLite , HailoRT , EdgeTPU Compiler , TVM , or ONNX Runtime . Use techniques such as post-training quantization , pruning , distillation , and model slicing . ️ Embedded Integration & Inference Deployment Integrate ML runtimes in C/C++ or Python into firmware stacks built on RTOS or embedded Linux . Handle real-time sensor inputs (camera, accelerometer, microphone) and pass them through inference engines. Manage memory, DMA transfers, inference buffers, and timing loops for deterministic behavior. Benchmarking & Performance Tuning Profile and optimize models for latency, memory usage, compute load , and power draw . Work with runtime logs, inference profilers, and vendor SDKs to squeeze maximum throughput on edge hardware. Conduct accuracy vs performance trade-off studies for different model variants. Testing & Validation Design unit, integration, and hardware-in-loop (HIL) tests to validate model execution on actual devices. Collaborate with hardware and firmware teams to debug runtime crashes, inference failures, and edge cases. Build reproducible benchmarking scripts and test data pipelines. Required Qualifications ‍ Education: BE/B.Tech/M.Tech in Electronics, Embedded Systems, Computer Science, or related disciplines. Experience: 2–4 years in embedded ML, edge AI, or firmware development with ML inference integration. Technical Skills Required Embedded Firmware & Runtime Strong experience in C/C++ , basic Python scripting. Experience with RTOS (FreeRTOS, Zephyr) or embedded Linux. Understanding of memory-mapped I/O, ring buffers, circular queues, and real-time execution cycles. ML Model Toolchains Experience with TensorFlow Lite , ONNX Runtime , HailoRT , EdgeTPU , uTensor , or TinyML . Knowledge of quantization-aware training or post-training quantization techniques. Familiarity with model conversion pipelines and hardware-aware model profiling. Media & Sensor Stack Ability to work with input/output streams from cameras , IMUs , microphones , etc. Experience integrating inference with V4L2, GStreamer, or custom ISP preprocessors is a plus. Tooling & Debugging Git, Docker, cross-compilation toolchains (Yocto, CMake). Debugging with SWD/JTAG, GDB, or serial console-based logging. Profiling with memory maps, timing charts, and inference logs. Preferred (Bonus) Skills Previous work with low-power vision devices , audio keyword spotting , or sensor fusion ML . Familiarity with edge security (encrypted models, secure firmware pipelines). Hands-on with simulators/emulators for ML testing (Edge Impulse, Hailo’s HEF emulator, etc.). Participation in TinyML forums , open-source ML toolkits, or ML benchmarking communities. Why Join Us? At EURTH TECHTRONICS PVT LTD , we're not just building IoT firmware—we're deploying machine learning intelligence on ultra-constrained edge platforms , powering real-time decisions at the edge. Get exposure to full-stack embedded ML pipelines — from model quantization to runtime integration. Work with a world-class team focused on ML efficiency, power optimization, and embedded system scalability .️ Contribute to mission-critical products used in industrial automation, medical wearables, smart infrastructure , and more. How to Apply Send your updated resume + GitHub/portfolio links to: jobs@eurthtech.com About the Company About EURTH TECHTRONICS PVT LTD EURTH TECHTRONICS PVT LTD is a cutting-edge Electronics Product Design and Engineering firm specializing in embedded systems, IoT solutions, and high-performance hardware development. We provide end-to-end product development services—from PCB design, firmware development, and system architecture to manufacturing and scalable deployment. With deep expertise in embedded software, signal processing, AI-driven edge computing, RF communication, and ultra-low-power design, we build next-generation industrial automation, consumer electronics, and smart infrastructure solutions. Our Core Capabilities Embedded Systems & Firmware Engineering – Architecting robust, real-time embedded solutions with RTOS, Linux, and MCU/SoC-based firmware. IoT & Wireless Technologies – Developing LoRa, BLE, Wi-Fi, UWB, and 5G-based connected solutions for industrial and smart city applications. Hardware & PCB Design – High-performance PCB layout, signal integrity optimization, and design for manufacturing (DFM/DFA). Product Prototyping & Manufacturing – Accelerating concept-to-market with rapid prototyping, design validation, and scalable production. AI & Edge Computing – Implementing real-time AI/ML on embedded devices for predictive analytics, automation, and security. Security & Cryptography – Integrating post-quantum cryptography, secure boot, and encrypted firmware updates. Our Industry Impact ✅ IoT & Smart Devices – Powering the next wave of connected solutions for industrial automation, logistics, and smart infrastructure. ✅ Medical & Wearable Tech – Designing low-power biomedical devices with precision sensor fusion and embedded intelligence. ✅ Automotive & Industrial Automation – Developing AI-enhanced control systems, predictive maintenance tools, and real-time monitoring solutions. ✅ Scalable Enterprise & B2B Solutions – Delivering custom embedded hardware and software tailored to OEMs, manufacturers, and system integrators. Our Vision We are committed to advancing technology and innovation in embedded product design. With a focus on scalability, security, and efficiency, we empower businesses with intelligent, connected, and future-ready solutions. We currently cater to B2B markets, offering customized embedded development services, with a roadmap to expand into direct-to-consumer (B2C) solutions.

Remote What We Offer: Canteen Subsidy Night Shift allowance as per process Health Insurance Tuition Reimbursement Work-Life Balance Initiatives Rewards & Recognition What You’ll Be Doing: Design, build, and deploy LLM-driven applications (e.g., document summarization, RAG-based QA, chatbots). Work with open-source LLMs using platforms like Ollama and Hugging Face. Implement Lang Chain and Lang Graph workflows for multi-step, multi-agent task resolution. Build and optimize RAG (Retrieval-Augmented Generation) systems using vector databases. Collaborate with cross-functional teams to ship features to production. Stay up to date with the latest in open-source LLMs, model optimization (LoRA, quantization), and multi-modal AI. What We Expect You To Have: 3–5 years of hands-on experience in AI/ML engineering. Proficient in Python, PyTorch, and Hugging Face Transformers. Proven experience with Lang Chain and Lang Graph for LLM workflows. Familiarity with Ollama, Mistral, LLaMA, or similar open-source LLMs. Experience working with vector stores (Qdrant, Pinecone, Weaviate, FAISS). Skilled in backend integration using FastAPI, Docker, and cloud platforms. Solid grasp of NLP, LLM reasoning, prompt engineering, and document parsing. Experience with LangServe, OpenAI tool/function calling, or agent orchestration. Background in multi-modal AI (e.g., image + text analysis). Familiarity with MLOps tools (MLflow, Weights & Biases, Airflow). Contributions to open-source GenAI projects. Understanding LLM safety, security, and alignment principles. Job Title : AI Engineer Location : Gandhinagar Schedule & Shift : 2:30 PM to 11:30 PM IST

Company Description Quantanite is a customer experience (CX)solutions company that helpsfast-growing companies and leading global brandsto transformand grow. We do thisthrough a collaborative and consultative approach,rethinking business processes and ensuring our clients employ the optimalmix of automationand human intelligence.We are an ambitiousteamof professionals spread acrossfour continents and looking to disrupt ourindustry by delivering seamless customerexperiencesforour clients,backed-upwithexceptionalresults.We havebig dreams, and are constantly looking for new colleaguesto join us who share our values, passion and appreciationfordiversity. Job Description About the Role: We are seeking a highly skilled Senior AI Engineer with deep expertise in Agentic frameworks, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, MLOps/LLMOps, and end-to-end GenAI application development. In this role, you will design, develop, fine-tune, deploy, and optimize state-of-the-art AI solutions across diverse enterprise use cases including AI Copilots, Summarization, Enterprise Search, and Intelligent Tool Orchestration. Key Responsibilities: Develop and Fine-Tune LLMs (e.g., GPT-4, Claude, LLaMA, Mistral, Gemini) using instruction tuning, prompt engineering, chain-of-thought prompting, and fine-tuning techniques. Build RAG Pipelines: Implement Retrieval-Augmented Generation solutions leveraging embeddings, chunking strategies, and vector databases like FAISS, Pinecone, Weaviate, and Qdrant. Implement and Orchestrate Agents: Utilize frameworks like MCP, OpenAI Agent SDK, LangChain, LlamaIndex, Haystack, and DSPy to build dynamic multi-agent systems and serverless GenAI applications. Deploy Models at Scale: Manage model deployment using HuggingFace, Azure Web Apps, vLLM, and Ollama, including handling local models with GGUF, LoRA/QLoRA, PEFT, and Quantization methods. Integrate APIs: Seamlessly integrate with APIs from OpenAI, Anthropic, Cohere, Azure, and other GenAI providers. Ensure Security and Compliance: Implement guardrails, perform PII redaction, ensure secure deployments, and monitor model performance using advanced observability tools. Optimize and Monitor: Lead LLMOps practices focusing on performance monitoring, cost optimization, and model evaluation. Work with AWS Services: Hands-on usage of AWS Bedrock, SageMaker, S3, Lambda, API Gateway, IAM, CloudWatch, and serverless computing to deploy and manage scalable AI solutions. Contribute to Use Cases: Develop AI-driven solutions like AI copilots, enterprise search engines, summarizers, and intelligent function-calling systems. Cross-functional Collaboration: Work closely with product, data, and DevOps teams to deliver scalable and secure AI products. Qualifications Required Skills and Experience: 3-5 years of experience in AI/ML roles, focusing on LLM agent development, data science workflows, and system deployment. Demonstrated experience in designing domain-specific AI systems and integrating structured/unstructured data into AI models. Proficiency in designing scalable solutions using LangChain and vector databases. Deep knowledge of LLMs and foundational models (GPT-4, Claude, Mistral, LLaMA, Gemini). Strong expertise in Prompt Engineering, Chain-of-Thought reasoning, and Fine-Tuning methods. Proven experience building RAG pipelines and working with modern vector stores (FAISS, Pinecone, Weaviate, Qdrant). Hands-on proficiency in LangChain, LlamaIndex, Haystack, and DSPy frameworks. Model deployment skills using HuggingFace, vLLM, Ollama, and handling LoRA/QLoRA, PEFT, GGUF models. Practical experience with AWS serverless services: Lambda, S3, API Gateway, IAM, CloudWatch. Strong coding ability in Python or similar programming languages. Experience with MLOps/LLMOps for monitoring, evaluation, and cost management. Familiarity with security standards: guardrails, PII protection, secure API interactions. Use Case Delivery Experience: Proven record of delivering AI Copilots, Summarization engines, or Enterprise GenAI applications. Additional Information Preferred Skills: Experience in BPO or IT Outsourcing environments. Knowledge of workforce management tools and CRM integrations. Hands-on experience with AI technologies and their applications in data analytics. Familiarity with Agile/Scrum methodologies. Soft Skills: Strong analytical and problem-solving capabilities. Excellent communication and stakeholder management skills. Ability to thrive in a fast-paced, dynamic environment.

Description: JOB RESPONSIBILITY • Collaborate with cross-functional teams, including data scientists and product managers, to acquire, process, and manage data for AI/ML model integration and optimization. • Design and implement robust, scalable, and enterprise-grade data pipelines to support state-of-the-art AI/ML models. • Debug, optimize, and enhance machine learning models, ensuring quality assurance and performance improvements. • Operate container orchestration platforms like Kubernetes, with advanced configurations and service mesh implementations, for scalable ML workload deployments. • Design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment. • Engage in advanced prompt engineering and fine-tuning of large language models (LLMs), focusing on semantic retrieval and chatbot development. • Document model architectures, hyperparameter optimization experiments, and validation results using version control and experiment tracking tools like MLflow or DVC. • Research and implement cutting-edge LLM optimization techniques, such as quantization and knowledge distillation, ensuring efficient model performance and reduced computational costs. • Collaborate closely with stakeholders to develop innovative and effective natural language processing solutions, specializing in text classification, sentiment analysis, and topic modeling. • Stay up-to-date with industry trends and advancements in AI technologies, integrating new methodologies and frameworks to continually enhance the AI engineering function. • Contribute to creating specialized AI solutions in healthcare, leveraging domain-specific knowledge for task adaptation and deployment. QUALIFICATION Minimum education: Bachelor’s degree in any Engineering Stream Specialized training, certifications, and/or other special requirements: Nice to have Preferred education: Computer Science/Engineering. EXPERIENCE Minimum relevant experience - 4+ years in AI Engineering SKILLS AND COMPETENCIES Technical Skills: • Advanced proficiency in Python with expertise in data science libraries (NumPy, Pandas, scikit-learn) and deep learning frameworks (PyTorch, TensorFlow) • Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) and prompt engineering techniques • Experience with big data processing using Spark for large-scale data analytics • Version control and experiment tracking using Git and MLflow • Software Engineering & Development: Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing. • DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations. • LLM Infrastructure & Deployment: Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management. • MLOps & Deployment: Utilization of containerization strategies for ML workloads, experience with model serving tools like TorchServe or TF Serving, and automated model retraining. • Cloud & Infrastructure: Strong grasp of advanced cloud services (AWS, GCP, Azure) and network security for ML systems. • LLM Project Experience: Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security. • General Skills: Python, SQL, knowledge of machine learning frameworks (Hugging Face, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP. • Experience in creating LLD for the provided architecture. • Experience working in microservices based architecture. Domain Expertise: • Strong mathematical foundation in statistics, probability, linear algebra, and optimization • Deep understanding of ML and LLM development lifecycle, including fine-tuning and evaluation • Expertise in feature engineering, embedding optimization, and dimensionality reduction • Advanced knowledge of A/B testing, experimental design, and statistical hypothesis testing • Experience with RAG systems, vector databases, and semantic search implementation • Proficiency in LLM optimization techniques including quantization and knowledge distillation • Understanding of MLOps practices for model deployment and monitoring Professional Competencies: • Strong analytical thinking with ability to solve complex ML challenges • Excellent communication skills for presenting technical findings to diverse audiences • Experience translating business requirements into data science solutions • Project management skills for coordinating ML experiments and deployments • Strong collaboration abilities for working with cross-functional teams • Dedication to staying current with latest ML research and best practices • Ability to mentor and share knowledge with team members

As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all. As a Qualcomm Software Engineer, you will design, develop, create, modify, and validate embedded and cloud edge software, applications, and/or specialized utility programs that launch cutting-edge, world class products that meet and exceed customer needs. Qualcomm Software Engineers collaborate with systems, hardware, architecture, test engineers, and other teams to design system-level software solutions and obtain information on performance requirements and interfaces. Machine Learning Engineer Job Location: Hyderabad More details below: Join a new and growing team at Qualcomm focused on advancing state-of-the-art in Machine Learning. The team uses Qualcomm chips’ extensive heterogeneous computing capabilities. See your work directly impact billions of mobile devices around the world. In this position, you will be responsible for the development and commercialization of ML solutions like Snapdragon Neural Processing Engine (SNPE) and AI Model Efficiency Toolkit (AIMET) on Qualcomm SoCs. You will have expert knowledge of design, improvement, and maintenance of large AI software stacks using best practices. Work Experience: 1. 8-12 years of relevant work experience in software development 2. Live and breathe quality software development with excellent analytical and debugging skills. Strong understanding of Deep Learning and Machine learning theory and practice. 3. Experience with Deep learning model development. Data transformations, model training, model design, model optimization. 4. Familiarity with various deep learning architectures and problem domains like Computer Vision, Speech recognition, NLP etc. 5. Strong development skills in Python and C++. Experience with at least one machine learning framework like TensorFlow, ONNX, Pytorch, etc. 6. Understanding of software development and debugging in embedded environments. 7. Excellent communication skills (verbal, presentation, written) 8. Ability to collaborate across a globally diverse team and multiple interests. Preferred Qualifications 1. Familiarity with neural network operators and model formats including PyTorch, ONNX, and Tensorflow. 2. Familiarity with neural network optimization techniques like graph optimization, quantization, pruning, knowledge distillation, network architecture search etc. 3. Strong understanding about embedded systems, system design fundamentals. 4. Well versed in version control tools like git 5. Experience with machine learning accelerators, optimizing algorithms for hardware acceleration cores, working with heterogeneous or parallel computing systems. Educational Requirements Bachelor's/Master’s/PhD in Computer Science, Computer Engineering, or Electrical Engineering

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Principal / Senior Systems Performance Engineer Micron Data Center and Client Workload Engineering in Hyderabad, India, is seeking a senior/principal engineer to join our dynamic team. The successful candidate will primarily contribute to the ML development, ML DevOps, HBM program in the data center by analyzing how AI/ML workloads perform on the latest MU-HBM, Micron main memory, expansion memory and near memory (HBM/LP) solutions, conduct competitive analysis, showcase the benefits that workloads see with MU-HBM’s capacity / bandwidth / thermals, contribute to marketing collateral, and extract AI/ML workload traces to help optimize future HBM designs. Job Responsibilities: The Job Responsibilities include but are not limited to the following: Design, implement, and maintain scalable & reliable ML infrastructure and pipelines. Collaborate with data scientists and ML engineers to deploy machine learning models into production environments. Automate and optimize ML workflows, including data preprocessing, model training, evaluation, and deployment. Monitor and manage the performance, reliability, and scalability of ML systems. Troubleshoot and resolve issues related to ML infrastructure and deployments. Implement and manage distributed training and inference solutions to enhance model performance and scalability. Utilize DeepSpeed, TensorRT, vLLM for optimizing and accelerating AI inference and training processes. Understand key care abouts when it comes to ML models such as: transformer architectures, precision, quantization, distillation, attention span & KV cache, MoE, etc. Build workload memory access traces from AI models. Study system balance ratios for DRAM to HBM in terms of capacity and bandwidth to understand and model TCO. Study data movement between CPU, GPU and the associated memory subsystems (DDR, HBM) in heterogeneous system architectures via connectivity such as PCIe/NVLINK/Infinity Fabric to understand the bottlenecks in data movement for different workloads. Develop an automated testing framework through scripting. Customer engagements and conference presentations to showcase findings and develop whitepapers. Requirements: Strong programming skills in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, or scikit-learn. Experience in data preparation: cleaning, splitting, and transforming data for training, validation, and testing. Proficiency in model training and development: creating and training machine learning models. Expertise in model evaluation: testing models to assess their performance. Skills in model deployment: launching server, live inference, batched inference Experience with AI inference and distributed training techniques. Strong foundation in GPU and CPU processor architecture Familiarity with and knowledge of server system memory (DRAM) Strong experience with benchmarking and performance analysis Strong software development skills using leading scripting, programming languages and technologies (Python, CUDA, C, C++) Familiarity with PCIe and NVLINK connectivity Preferred Qualifications: Experience in quickly building AI workflows: building pipelines and model workflows to design, deploy, and manage consistent model delivery. Ability to easily deploy models anywhere: using managed endpoints to deploy models and workflows across accessible CPU and GPU machines. Understanding of MLOps: the overarching concept covering the core tools, processes, and best practices for end-to-end machine learning system development and operations in production. Knowledge of GenAIOps: extending MLOps to develop and operationalize generative AI solutions, including the management of and interaction with a foundation model. Familiarity with LLMOps: focused specifically on developing and productionizing LLM-based solutions. Experience with RAGOps: focusing on the delivery and operation of RAGs, considered the ultimate reference architecture for generative AI and LLMs. Data management: collect, ingest, store, process, and label data for training and evaluation. Configure role-based access control; dataset search, browsing, and exploration; data provenance tracking, data logging, dataset versioning, metadata indexing, data quality validation, dataset cards, and dashboards for data visualization. Workflow and pipeline management: work with cloud resources or a local workstation; connect data preparation, model training, model evaluation, model optimization, and model deployment steps into an end-to-end automated and scalable workflow combining data and compute. Model management: train, evaluate, and optimize models for production; store and version models along with their model cards in a centralized model registry; assess model risks, and ensure compliance with standards. Experiment management and observability: track and compare different machine learning model experiments, including changes in training data, models, and hyperparameters. Automatically search the space of possible model architectures and hyperparameters for a given model architecture; analyze model performance during inference, monitor model inputs and outputs for concept drift. Synthetic data management: extend data management with a new native generative AI capability. Generate synthetic training data through domain randomization to increase transfer learning capabilities. Declaratively define and generate edge cases to evaluate, validate, and certify model accuracy and robustness. Embedding management: represent data samples of any modality as dense multi-dimensional embedding vectors; generate, store, and version embeddings in a vector database. Visualize embeddings for improvised exploration. Find relevant contextual information through vector similarity search for RAGs. Education: Bachelor’s or higher (with 12+ years of experience) in Computer Science or related field. About Micron Technology, Inc. We are an industry leader in innovative memory and storage solutions transforming how the world uses information to enrich life for all . With a relentless focus on our customers, technology leadership, and manufacturing and operational excellence, Micron delivers a rich portfolio of high-performance DRAM, NAND, and NOR memory and storage products through our Micron® and Crucial® brands. Every day, the innovations that our people create fuel the data economy, enabling advances in artificial intelligence and 5G applications that unleash opportunities — from the data center to the intelligent edge and across the client and mobile user experience. To learn more, please visit micron.com/careers All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. To request assistance with the application process and/or for reasonable accommodations, please contact hrsupport_india@micron.com Micron Prohibits the use of child labor and complies with all applicable laws, rules, regulations, and other international and industry labor standards. Micron does not charge candidates any recruitment fees or unlawfully collect any other payment from candidates as consideration for their employment with Micron. AI alert : Candidates are encouraged to use AI tools to enhance their resume and/or application materials. However, all information provided must be accurate and reflect the candidate's true skills and experiences. Misuse of AI to fabricate or misrepresent qualifications will result in immediate disqualification. Fraud alert: Micron advises job seekers to be cautious of unsolicited job offers and to verify the authenticity of any communication claiming to be from Micron by checking the official Micron careers website in the About Micron Technology, Inc.