319 Quantization Jobs - Page 2

Company Size Large-scale / Global Experience Required 3 - 5 years Working Days 6 days/week Office Location Viman Nagar, Pune Role & Responsibilities Agentic AI Development: Design and develop multi-agent conversational frameworks with adaptive decision-making capabilities. Integrate goal-oriented reasoning and memory components into agents using transformer-based architectures. Build negotiation-capable bots with real-time context adaptation and recursive feedback processing. Generative AI & Model Optimization: Fine-t une LLMs/SLMs using proprietary and domain-specific datasets (NBFC, Financial Services, etc.). Apply distillation and quantization for efficient deployment on edge devices. Benchmark LLM/SLM performance on server vs. edge environments for real-time use cases. Speech And Conversational Intelligence: Implement contextual dialogue flows using speech inputs with emotion and intent tracking. Evaluate and deploy advanced Speech-to-Speech (S2S) models for naturalistic voice responses. Work on real-time speaker diarization and multi-turn, multi-party conversation tracking. Voice Biometrics & AI Security: Train and evaluate voice biometric models for secure identity verification. Implement anti-spoofing layers to detect deepfakes, replay attacks, and signal tampering. Ensure compliance with voice data privacy and ethical AI guidelines. Self-Learning & Autonomous Adaptation: Develop frameworks for agents to self-correct and adapt using feedback loops without full retraining. Enable low-footprint learning systems on-device to support personalization on the edge. Ideal Candidate Educational Qualifications: Bachelor’s/Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. Experience Required: 3–5 years of experience, with a mix of core software development and AI/ML model engineering. Proven hands-on work with Conversational AI, Generative AI, or Multi-Agent Systems. Technical Proficiency: Strong programming in Python, TensorFlow/PyTorch, and model APIs (Hugging Face, LangChain, OpenAI, etc.). Expertise in STT, TTS, S2S, speaker diarization, and speech emotion recognition. LLM fine-tuning, model optimization (quantization, distillation), RAG pipelines. Understanding of agentic frameworks, cognitive architectures, or belief-desire-intention (BDI) models. Familiarity with Edge AI deployment, low-latency model serving, and privacy-compliant data pipelines. Desirable: Exposure to agent-based simulation, reinforcement learning, or behavioralmodeling. Publications, patents, or open-source contributions in conversational AI or GenAI systems. Perks, Benefits and Work Culture Our people define our passion and our audacious, incredibly rewarding achievements. Bajaj Finance Limited is one of India’s most diversified Non-banking financial companies, and among Asia’s top 10 Large workplaces. If you have the drive to get ahead, we can help find you an opportunity at any of the 500+ locations we’re present in India. Skills: edge ai deployment,adaptation,speech emotion recognition,llm fine-tuning,tensorflow,models,intelligence,model optimization,speech,speech-to-speech,data,openai,hugging face,llm,optimization,agents,langchain,pytorch,python

Job Description – AI Developer (Agentic AI Frameworks, Computer Vision & LLMs) Location (Hybrid - Bangalore) About the Role We’re seeking an AI Developer who specializes in agentic AI frameworks —LangChain, LangGraph, CrewAI, or equivalents—and who can take both vision and language models from prototype to production. You will lead the design of multi‑agent systems that coordinate perception (image classification & extraction), reasoning, and action, while owning the end‑to‑end deep‑learning life‑cycle (training, scaling, deployment, and monitoring). Key Responsibilities Scope What You’ll Do Agentic AI Frameworks (Primary Focus) Architect and implement multi‑agent workflows using LangChain, LangGraph, CrewAI, or similar. Design role hierarchies, state graphs, and tool integrations that enable autonomous data processing, decision‑making, and orchestration. Benchmark and optimize agent performance (cost, latency, reliability). Image Classification & Extraction Build and fine‑tune CNN/ViT models for classification, detection, OCR, and structured data extraction. Create scalable data‑ingestion, labeling, and augmentation pipelines. LLM Fine‑Tuning & Retrieval‑Augmented Generation (RAG) Fine‑tune open‑weight LLMs with LoRA/QLoRA, PEFT; perform SFT, DPO, or RLHF as needed. Implement RAG pipelines using vector databases (FAISS, Weaviate, pgvector) and domain‑specific adapters. Deep Learning at Scale Develop reproducible training workflows in PyTorch/TensorFlow with experiment tracking (MLflow, W&B). Serve models via TorchServe/Triton/KServe on Kubernetes, SageMaker, or GCP Vertex AI. MLOps & Production Excellence Build robust APIs/micro‑services (FastAPI, gRPC). Establish CI/CD, monitoring (Prometheus, Grafana), and automated retraining triggers. Optimize inference on CPU/GPU/Edge with ONNX/TensorRT, quantization, and pruning. Collaboration & Mentorship Translate product requirements into scalable AI services. Mentor junior engineers, conduct code and experiment reviews, and evangelize best practices. Minimum Qualifications B.S./M.S. in Computer Science, Electrical Engineering, Applied Math, or related discipline. 5+ years building production ML/DL systems with strong Python & Git . Demonstrable expertise in at least one agentic AI framework (LangChain, LangGraph, CrewAI, or comparable). Proven delivery of computer‑vision models for image classification/extraction. Hands‑on experience fine‑tuning LLMs and deploying RAG solutions. Solid understanding of containerization (Docker) and cloud AI stacks (AWS/Azure). Knowledge of distributed training, GPU acceleration, and performance optimization. ---------------------------------------------------------------------------------------------------------------------------------------------------------- Job Type: Full-time Pay: Up to ₹1,200,000.00 per year Experience: AI, LLM, RAG: 4 years (Preferred) Vector database, Image classification: 4 years (Preferred) containerization (Docker): 3 years (Preferred) ML/DL systems with strong Python & Git: 3 years (Preferred) LangChain, LangGraph, CrewAI: 3 years (Preferred) Location: Bangalore, Karnataka (Preferred) Work Location: In person

Job Description: We are seeking a highly skilled and experienced C++ Engineer to join our team. The primary responsibility will be converting existing Python-based computer vision and deep learning (CVDL) code into optimized, production-ready C++ code. The ideal candidate should be proficient in working with C++ frameworks and libraries, including TensorFlow, PyTorch, ONNX, MNN, NCNN, TensorFlow Lite (TFLite), MMDeploy, etc. The resulting C++ code will be used across Windows and Ubuntu environments, with a strong emphasis on cross-platform compatibility and performance optimization. Key Responsibilities: Convert Python-based CVDL (Computer Vision and Deep Learning) pipelines into optimized C++ implementations. Implement models and algorithms using C++ frameworks such as TensorFlow, PyTorch, ONNX, MNN, NCNN, TensorFlow Lite (TFLite), MMDeploy, and other relevant libraries. Optimize code for performance, ensuring efficient use of resources, especially in real-time processing pipelines. Ensure cross-platform compatibility, building C++ code that works seamlessly on both Windows and Ubuntu using CMakeLists. Debug, profile, and optimize deep learning inference pipelines, addressing issues related to memory usage, speed, and accuracy. Collaborate with AI teams to understand the Python codebase, gather requirements, and ensure the successful porting of features. Maintain up-to-date knowledge of the latest developments in C++ frameworks, deep learning inference engines, and performance optimization techniques. Requirements: Experience: - 5+ years of experience in C++ software development, specifically in converting Python code into C++. - 3 + years of experience with computer vision and deep learning frameworks such as TensorFlow, PyTorch, ONNX, MNN, NCNN, TensorFlow Lite (TFLite), MMDeploy , Mediapipe and Bazel build system. - Solid experience in cross-platform development for both Windows and Ubuntu using CMakeLists. Programming Skills: - Proficiency in C++ (C++11/14/17) with a deep understanding of memory management, multi-threading, and performance optimization. - Familiarity with Python, specifically in computer vision and deep learning applications, to interpret and convert code accurately. - Strong knowledge of CMake for building cross-platform applications. Technical Expertise: - Experience working with deep learning models and converting models between different formats (e.g., TensorFlow to ONNX, PyTorch to NCNN, etc.). - Experience with OpenCV and other related computer vision libraries. - Understanding of inference optimizations such as quantization, pruning, and model acceleration will be plus. Communication: - Strong problem-solving skills and the ability to work in a collaborative, fast-paced environment. - Ability to communicate effectively with cross-functional teams, including data scientists, ML engineers, and Python developers.

About the Role We are seeking a visionary and technically astute Lead AI Architect to lead the architecture and design of scalable AI systems and next-generation intelligent platforms. As a core member of the leadership team, you will be responsible for driving end-to-end architectural strategy, model optimization, and AI infrastructure that powers mission-critical solutions across our product lines. This is a foundational role for someone passionate about architecting solutions involving RAG , SLMs/LLMs , multi-agent systems , and scalable model pipelines across cloud-native environments. Salary 30 - 45 LPA with additional benefits. Key Responsibilities Define and own the AI/ML architectural roadmap , aligning with product vision and technical goals. Architect and oversee implementation of RAG-based solutions , LLM/SLM fine-tuning pipelines , and multi-agent orchestration . Lead design of model training and inference pipelines ensuring scalability, modularity, and observability. Evaluate and select open-source and proprietary foundation models for fine-tuning, instruction tuning, and domain adaptation. Guide integration of vector databases, semantic search, and prompt orchestration frameworks (LangChain, LlamaIndex, etc.). Ensure best practices in model deployment, versioning, monitoring , and performance optimization (GPU utilization, memory efficiency, etc.). Collaborate with Engineering, DevOps, Product, and Data Science teams to bring AI features to production. Mentor mid-level engineers and interns; contribute to technical leadership and code quality . Maintain awareness of latest research, model capabilities, and trends in AI. Required Skills & Qualifications 6+ years of hands-on experience in AI/ML architecture and model deployment. Expert-level knowledge of Python and libraries such as PyTorch, Hugging Face Transformers, scikit-learn, and FastAPI. Deep understanding of LLMs/SLMs, embedding models, tokenization strategies, fine-tuning, quantization, and LoRA/QLoRA. Proven experience with Retrieval-Augmented Generation (RAG) pipelines and vector DBs like FAISS, Pinecone, or Weaviate. Strong grasp of system design, distributed training, MLOps, and scalable cloud-based infrastructure (AWS/GCP/Azure). Experience with containerization (Docker), orchestration (Kubernetes), and experiment tracking (MLFlow, W&B). Experience in building secure and performant REST APIs , deploying and monitoring AI services in production. Nice to Have Exposure to multi-agent frameworks, task planners, or LangGraph. Experience leading AI platform teams or architecting enterprise-scale ML platforms. Familiarity with Data Governance, Responsible AI, and model compliance requirements. Published papers, open-source contributions, or patents in the AI/ML domain. Why Join Us Be at the forefront of innovation in AI and language intelligence. Influence strategic technical decisions and drive company-wide AI architecture. Lead a growing AI team in a high-impact, fast-paced environment. Competitive compensation, equity options, and leadership opportunity.

About the job Location: Bangalore Career Level: IC3 Oracle Cloud Infrastructure (OCI) is at the forefront of cloud innovation, blending the agility of a startup with the reliability of a leading enterprise software provider. Our AI Science team pioneers state-of-the-art machine learning solutions that empower customers and solve complex real-world problems at scale. We’re looking for an experienced Sr. Applied Science (IC3) with deep hand-on experience in Generative AI and Computer Vision area to develop highly complex and accurate data science model. In this role, you will develop of secure, scalable, and innovative AI solutions leveraging cutting-edge techniques in computer vision, Large multimodal models and other GenAI technologies. As a Senior Applied Scientist, you will develop and deploy state-of-the-art computer vision solutions leveraging generative AI technologies such as Large multimodal models and computer vision technologies such as image classification, object detection, vision grounding etc. This individual contributor(IC) role will build best-in-class computer vision solutions at scale. is perfect for a hands-on data science architecture design and is eager to drive innovation and excellence in AI and computer vision area. You will partner with the Product and Engineering managers to influence strategic decisions, drive experimentation and communicate results to higher managements. You will build best-in-class LLM/LMM/computer vision solutions for the Oracle business domain at scale. You will also partner with Product Management, Data Labelling and Engineering teams to get to develop build best-in-class computer vision solutions at scale. The ideal candidate has extensive experience with computer vision techniques, deep learning techniques, model serving, and a demonstrated ability to think strategicallyabout business, product, and technical challenges to contribute to the development of current and future vision services. Key Responsibilities Development of advanced AI models and algorithms, focusing on large language model, large multimodal, computer vision and foundation models. Design, implement and test the critical module/features of AI service that are correct, highly available, scalable, and cost-effective. Champion best practices for testing, benchmarking, and model validation to ensure reliability and performance. Analysis of ML models, and optimizing models for accuracy and latency. Large-scale training & production deployment with ML models. Own data analysis, feature engineering, technique selection & implementation, debugging, and maintenance of production model. Experience implementing machine learning algorithms or research papers from scratch to production. Work with large, complex data sets. Proactively identify the technical issues/bugs and provide innovative solutions. File patent and publication as by product of solving complex business problems Partner closely with product managers, engineering leads, and annotation/data teams to define requirements, data quality assurance and acceptance of data/annotation as required. Leverage Oracle Cloud technology. Preferred Qualifications Ph.D. (preferred) or Master’s in Computer Science, Machine Learning, Computer Vision, or related field. PhD in computer vision or 2+ years of Experience designing, implementing and deploying computer vision models in production environments Expertise in GenAI, LLMs, LMMs, object detection, facial recognition, and image classification. Strong foundation in deep learning architectures such as CNNs, transformers, diffusion models, and multimodal models. Expert in at least one high level language such as Python/Java/C++ Practical experience in ML algorithm design, model training and production deployment using microservices architecture Practical experience working in a cloud environment: Oracle Cloud (OCI), AWS, GCP, Azure or similar technology. Experience or willingness to learn and work in Agile and iterative development processes. Strong drive to learn and master new technologies and techniques. Deep understanding of data structures, algorithms, and excellent problem-solving skills. You enjoy a fast-paced work environment. Identify data science use cases and design scalable solutions that can be built as a feature of the product/service. Contributes to writing production model code. Work with Software Engineering teams to deploy them in production. Set up environment needed to run experiments for all projects. Set up distributed environments. Design and implement algorithms, train models, and deploy both to production to validate premises and achieve goals. Design and execute offline/online experiments and model performance testing. Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Address business/customer problems and questions using statistical and machine learning techniques to achieve business goals and KPI's. Come up with innovative solutions to address tradeoffs or challenges faced by team. Stay up-to date with research and trends regarding latest algorithms in ML or other industry/domain space. Perform research in emerging areas, which may include efficient neural network development including quantization, pruning, compression and neural architecture search, as well as novel differentiable compute primitives. May perform other duties as assigned.

Applied Machine Learning Scientist – Voice AI, NLP & GenAI Applications Location : Sector 63, Gurugram, Haryana – 100% In-Office Working Days : Monday to Friday, with 2nd and 4th Saturdays off Working Hours : 10:30 AM – 8:00 PM Experience : 3–7 years in applied ML, with at least 2 years focused on voice, NLP, or GenAI deployments Function : AI/ML Research & Engineering | Conversational Intelligence | Real-time Model Deployment Apply : careers@darwix.ai Subject Line : “Application – Applied ML Scientist – [Your Name]” About Darwix AI Darwix AI is a GenAI-powered platform transforming how enterprise sales, support, and credit teams engage with customers. Our proprietary AI stack ingests data across calls, chat, email, and CCTV streams to generate: Real-time nudges for agents and reps Conversational analytics and scoring to drive performance CCTV-based behavior insights to boost in-store conversion We’re live across leading enterprises in India and MENA, including IndiaMart, Wakefit, Emaar, GIVA, Bank Dofar , and others. We’re backed by top-tier operators and venture investors and scaling rapidly across multiple verticals and geographies. Role Overview We are looking for a hands-on, impact-driven Applied Machine Learning Scientist to build, optimize, and productionize AI models across ASR, NLP, and LLM-driven intelligence layers . This is a core role in our AI/ML team where you’ll be responsible for building the foundational ML capabilities that drive our real-time sales intelligence platform. You will work on large-scale multilingual voice-to-text pipelines, transformer-based intent detection, and retrieval-augmented generation systems used in live enterprise deployments. Key ResponsibilitiesVoice-to-Text (ASR) Engineering Deploy and fine-tune ASR models such as WhisperX, wav2vec 2.0, or DeepSpeech for Indian and GCC languages Integrate diarization and punctuation recovery pipelines Benchmark and improve transcription accuracy across noisy call environments Optimize ASR latency for real-time and batch processing modes NLP & Conversational Intelligence Train and deploy NLP models for sentence classification, intent tagging, sentiment, emotion, and behavioral scoring Build call scoring logic aligned to domain-specific taxonomies (sales pitch, empathy, CTA, etc.) Fine-tune transformers (BERT, RoBERTa, etc.) for multilingual performance Contribute to real-time inference APIs for NLP outputs in live dashboards GenAI & LLM Systems Design and test GenAI prompts for summarization, coaching, and feedback generation Integrate retrieval-augmented generation (RAG) using OpenAI, HuggingFace, or open-source LLMs Collaborate with product and engineering teams to deliver LLM-based features with measurable accuracy and latency metrics Implement prompt tuning, caching, and fallback strategies to ensure system reliability Experimentation & Deployment Own model lifecycle: data preparation, training, evaluation, deployment, monitoring Build reproducible training pipelines using MLflow, DVC, or similar tools Write efficient, well-structured, production-ready code for inference APIs Document experiments and share insights with cross-functional teams Required Qualifications Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related fields 3–7 years experience applying ML in production, including NLP and/or speech Experience with transformer-based architectures for text or audio (e.g., BERT, Wav2Vec, Whisper) Strong Python skills with experience in PyTorch or TensorFlow Experience with REST APIs, model packaging (FastAPI, Flask, etc.), and containerization (Docker) Familiarity with audio pre-processing, signal enhancement, or feature extraction (MFCC, spectrograms) Knowledge of MLOps tools for experiment tracking, monitoring, and reproducibility Ability to work collaboratively in a fast-paced startup environment Preferred Skills Prior experience working with multilingual datasets (Hindi, Arabic, Tamil, etc.) Knowledge of diarization and speaker separation algorithms Experience with LLM APIs (OpenAI, Cohere, Mistral, LLaMA) and RAG pipelines Familiarity with inference optimization techniques (quantization, ONNX, TorchScript) Contribution to open-source ASR or NLP projects Working knowledge of AWS/GCP/Azure cloud platforms What Success Looks Like Transcription accuracy improvement ≥ 85% across core languages NLP pipelines used in ≥ 80% of Darwix AI’s daily analyzed calls 3–5 LLM-driven product features delivered in the first year Inference latency reduced by 30–50% through model and infra optimization AI features embedded across all Tier 1 customer accounts within 12 months Life at Darwix AI You will be working in a high-velocity product organization where AI is core to our value proposition. You’ll collaborate directly with the founding team and cross-functional leads, have access to enterprise datasets, and work on ML systems that impact large-scale, real-time operations. We value rigor, ownership, and speed. Model ideas become experiments in days, and successful experiments become deployed product features in weeks. Compensation & Perks Competitive fixed salary based on experience Quarterly/Annual performance-linked bonuses ESOP eligibility post 12 months Compute credits and model experimentation environment Health insurance, mental wellness stipend Premium tools and GPU access for model development Learning wallet for certifications, courses, and AI research access Career Path Year 1: Deliver production-grade ASR/NLP/LLM systems for high-usage product modules Year 2: Transition into Senior Applied Scientist or Tech Lead for conversation intelligence Year 3: Grow into Head of Applied AI or Architect-level roles across vertical product lines How to Apply Email the following to careers@darwix.ai : Updated resume (PDF) A short write-up (200 words max): “How would you design and optimize a multilingual voice-to-text and NLP pipeline for noisy call center data in Hindi and English?” Optional: GitHub or portfolio links demonstrating your work Subject Line : “Application – Applied Machine Learning Scientist – [Your Name]”

Head of AI & ML Platforms Focus : Voice AI, NLP, Conversation Intelligence for Omnichannel Enterprise Sales Location : Sector 63, Gurugram, Haryana — Full-time, 100% In-Office Work Hours : 10:30 AM – 8:00 PM, Monday to Friday (2nd and 4th Saturdays off) Experience Required : 8–15 years in AI/ML, with 3+ years leading teams in voice, NLP, or conversation platforms Apply : careers@darwix.ai Subject Line : “Application – Head of AI & ML Platforms – [Your Name]” About Darwix AI Darwix AI is a GenAI-powered platform for enterprise revenue teams across sales, support, credit, and retail. Our proprietary AI stack ingests multimodal inputs—voice calls, chat logs, emails, and CCTV streams—and delivers contextual nudges, conversation scoring, and performance analytics in real time. Our suite of products includes: Transform+ : Real-time conversational intelligence for contact centers and field sales Sherpa.ai : A multilingual GenAI assistant that provides in-the-moment coaching, summaries, and objection handling support Store Intel : A computer vision solution that transforms CCTV feeds into actionable insights for physical retail spaces Darwix AI is trusted by large enterprises such as IndiaMart, Wakefit, Emaar, GIVA, Bank Dofar, and Sobha Realty , and is backed by leading institutional and operator investors. We are expanding rapidly across India, the Middle East, and Southeast Asia. Role Overview We are seeking a highly experienced and technically strong Head of AI & ML Platforms to architect and lead the end-to-end AI systems powering our voice intelligence, NLP, and GenAI solutions. This is a leadership role that blends research depth with applied engineering execution. The ideal candidate will have deep experience in building and deploying voice-to-text pipelines, multilingual NLP systems, and production-grade inference workflows. The individual will be responsible for model design, accuracy benchmarking, latency optimization, infrastructure orchestration, and integration across our product suite. This is a critical leadership role with direct influence over product velocity, enterprise client outcomes, and future platform scalability. Key ResponsibilitiesVoice-to-Text (ASR) Architecture Lead the design and optimization of large-scale automatic speech recognition (ASR) pipelines using open-source and commercial frameworks (e.g., WhisperX, Deepgram, AWS Transcribe) Enhance speaker diarization, custom vocabulary accuracy, and latency performance for real-time streaming scenarios Build fallback ASR workflows for offline and batch mode processing Implement multilingual and domain-specific tuning, especially for Indian and GCC languages Natural Language Processing and Conversation Analysis Build NLP models for conversation segmentation, intent detection, tone/sentiment analysis, and call scoring Implement multilingual support (Hindi, Arabic, Tamil, etc.) with fallback strategies for mixed-language and dialectal inputs Develop robust algorithms for real-time classification of sales behaviors (e.g., probing, pitching, objection handling) Train and fine-tune transformer-based models (e.g., BERT, RoBERTa, DeBERTa) and sentence embedding models for text analytics GenAI and LLM Integration Design modular GenAI pipelines for nudging, summarization, and response generation using tools like LangChain, LlamaIndex, and OpenAI APIs Implement retrieval-augmented generation (RAG) architectures for contextual, accurate, and hallucination-resistant outputs Build prompt orchestration frameworks that support real-time sales coaching across channels Ensure safety, reliability, and performance of LLM-driven outputs across use cases Infrastructure and Deployment Lead the development of scalable, secure, and low-latency AI services deployed via FastAPI, TorchServe, or similar frameworks Oversee model versioning, monitoring, and retraining workflows using MLflow, DVC, or other MLOps tools Build hybrid inference systems for batch, real-time, and edge scenarios depending on product usage Optimize inference pipelines for GPU/CPU balance, resource scheduling, and runtime efficiency Team Leadership and Cross-functional Collaboration Recruit, manage, and mentor a team of machine learning engineers and research scientists Collaborate closely with Product, Engineering, and Customer Success to translate product requirements into AI features Own AI roadmap planning, sprint delivery, and KPI measurement Serve as the subject-matter expert for AI-related client discussions, sales demos, and enterprise implementation roadmaps Required Qualifications 8+ years of experience in AI/ML with a minimum of 3 years in voice AI, NLP, or conversational platforms Proven experience delivering production-grade ASR or NLP systems at scale Deep familiarity with Python, PyTorch, HuggingFace, FastAPI, and containerized environments (Docker/Kubernetes) Expertise in fine-tuning LLMs and building multi-language, multi-modal intelligence stacks Demonstrated experience with tools such as WhisperX, Deepgram, Azure Speech, LangChain, MLflow, or Triton Inference Server Experience deploying real-time or near real-time inference models at enterprise scale Strong architectural thinking with the ability to design modular, reusable, and scalable ML services Track record of building and leading high-performing ML teams Preferred Skills Background in telecom, contact center AI, conversational analytics, or field sales optimization Familiarity with GPU deployment, model quantization, and inference optimization Experience with low-resource languages and multilingual data augmentation Understanding of sales enablement workflows and domain-specific ontology development Experience integrating AI models into customer-facing SaaS dashboards and APIs Success Metrics Transcription accuracy improvement by ≥15% across core languages within 6 months End-to-end voice-to-nudge latency reduced below 5 seconds GenAI assistant adoption across 70%+ of eligible conversations AI-driven call scoring rolled out across 100% of Tier 1 clients within 9 months Model deployment velocity (dev to prod) reduced by ≥40% through tooling and process improvements Culture at Darwix AI At Darwix AI, we operate at the intersection of engineering velocity and product clarity. We move fast, prioritize outcomes over optics, and expect leaders to drive hands-on impact. You will work directly with the founding team and senior leaders across engineering, product, and GTM functions. Expect ownership, direct communication, and a culture that values builders who scale systems, people, and strategy. Compensation and Benefits Competitive fixed compensation Performance-based bonuses and growth-linked incentives ESOP eligibility for leadership candidates Access to GPU/compute credits and model experimentation infrastructure Comprehensive medical insurance and wellness programs Dedicated learning and development budget for technical and leadership upskilling MacBook Pro, premium workstation, and access to industry tooling licenses Career Progression 12-month roadmap: Build and stabilize AI platform across all product lines 18–24-month horizon: Elevate to VP of AI or Chief AI Officer as platform scale increases globally Future leadership role in enabling new verticals (e.g., healthcare, finance, logistics) with domain-specific GenAI solutions How to Apply Send the following to careers@darwix.ai : Updated CV (PDF format) A short statement (200 words max) on: “How would you design a multilingual voice-to-text pipeline optimized for low-resource Indic languages, with real-time nudge delivery?” Links to any relevant GitHub repos, publications, or deployed projects (optional) Subject Line : “Application – Head of AI & ML Platforms – [Your Name]”

Job Description About the job Location: Bangalore Career Level: IC3 Oracle Cloud Infrastructure (OCI) is at the forefront of cloud innovation, blending the agility of a startup with the reliability of a leading enterprise software provider. Our AI Science team pioneers state-of-the-art machine learning solutions that empower customers and solve complex real-world problems at scale. We’re looking for an experienced Sr. Applied Science (IC3) with deep hand-on experience in Generative AI and Computer Vision area to develop highly complex and accurate data science model. In this role, you will develop of secure, scalable, and innovative AI solutions leveraging cutting-edge techniques in computer vision, Large multimodal models and other GenAI technologies. As a Senior Applied Scientist, you will develop and deploy state-of-the-art computer vision solutions leveraging generative AI technologies such as Large multimodal models and computer vision technologies such as image classification, object detection, vision grounding etc. This individual contributor(IC) role will build best-in-class computer vision solutions at scale. is perfect for a hands-on data science architecture design and is eager to drive innovation and excellence in AI and computer vision area. You will partner with the Product and Engineering managers to influence strategic decisions, drive experimentation and communicate results to higher managements. You will build best-in-class LLM/LMM/computer vision solutions for the Oracle business domain at scale. You will also partner with Product Management, Data Labelling and Engineering teams to get to develop build best-in-class computer vision solutions at scale. The ideal candidate has extensive experience with computer vision techniques, deep learning techniques, model serving, and a demonstrated ability to think strategicallyabout business, product, and technical challenges to contribute to the development of current and future vision services. Key Responsibilities Development of advanced AI models and algorithms, focusing on large language model, large multimodal, computer vision and foundation models. Design, implement and test the critical module/features of AI service that are correct, highly available, scalable, and cost-effective. Champion best practices for testing, benchmarking, and model validation to ensure reliability and performance. Analysis of ML models, and optimizing models for accuracy and latency. Large-scale training & production deployment with ML models. Own data analysis, feature engineering, technique selection & implementation, debugging, and maintenance of production model. Experience implementing machine learning algorithms or research papers from scratch to production. Work with large, complex data sets. Proactively identify the technical issues/bugs and provide innovative solutions. File patent and publication as by product of solving complex business problems Partner closely with product managers, engineering leads, and annotation/data teams to define requirements, data quality assurance and acceptance of data/annotation as required. Leverage Oracle Cloud technology. Preferred Qualifications Ph.D. (preferred) or Master’s in Computer Science, Machine Learning, Computer Vision, or related field. PhD in computer vision or 2+ years of Experience designing, implementing and deploying computer vision models in production environments Expertise in GenAI, LLMs, LMMs, object detection, facial recognition, and image classification. Strong foundation indeep learning architectures such as CNNs, transformers, diffusion models, and multimodal models. Expert in at least one high level language such as Python/Java/C++ Practical experience in ML algorithm design, model training and production deployment using microservices architecture Practical experience working in a cloud environment: Oracle Cloud (OCI), AWS, GCP, Azure or similar technology. Experience or willingness to learn and work in Agile and iterative development processes. Strong drive to learn and master new technologies and techniques. Deep understanding of data structures, algorithms, and excellent problem-solving skills. You enjoy a fast-paced work environment. Responsibilities Identify data science use cases and design scalable solutions that can be built as a feature of the product/service. Contributes to writing production model code. Work with Software Engineering teams to deploy them in production. Set up environment needed to run experiments for all projects. Set up distributed environments. Design and implement algorithms, train models, and deploy both to production to validate premises and achieve goals. Design and execute offline/online experiments and model performance testing. Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Address business/customer problems and questions using statistical and machine learning techniques to achieve business goals and KPI's. Come up with innovative solutions to address tradeoffs or challenges faced by team. Stay up-to date with research and trends regarding latest algorithms in ML or other industry/domain space. Perform research in emerging areas, which may include efficient neural network development including quantization, pruning, compression and neural architecture search, as well as novel differentiable compute primitives. May perform other duties as assigned. Qualifications Career Level - IC3 About Us As a world leader in cloud solutions, Oracle uses tomorrow’s technology to tackle today’s challenges. We’ve partnered with industry-leaders in almost every sector—and continue to thrive after 40+ years of change by operating with integrity. We know that true innovation starts when everyone is empowered to contribute. That’s why we’re committed to growing an inclusive workforce that promotes opportunities for all. Oracle careers open the door to global opportunities where work-life balance flourishes. We offer competitive benefits based on parity and consistency and support our people with flexible medical, life insurance, and retirement options. We also encourage employees to give back to their communities through our volunteer programs. We’re committed to including people with disabilities at all stages of the employment process. If you require accessibility assistance or accommodation for a disability at any point, let us know by emailing accommodation-request_mb@oracle.com or by calling +1 888 404 2494 in the United States. Oracle is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Oracle will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.

Technology->Artificial Intelligence->Computer Vision Job Overview: As a Lead Computer Vision Engineer, you will lead the development and deployment of cutting-edge computer vision models and solutions for a variety of applications including image classification, object detection, segmentation, and more. You will work closely with cross-functional teams to implement advanced computer vision algorithms, ensure the integration of AI solutions into products, and help guide the research and innovation of next-generation visual AI technologies. 2. Technical Skills: Deep Learning Frameworks: Proficiency in TensorFlow, PyTorch, or other deep learning libraries. Computer Vision Tools: Expertise in OpenCV, Dlib, and other image processing libraries. Model Deployment: Experience deploying models to production using platforms such as AWS, Google Cloud, or NVIDIA Jetson (for edge devices). Algorithms: Strong understanding of core computer vision techniques like image classification, object detection (YOLO, Faster R-CNN), image segmentation (U-Net), and feature extraction. Programming Languages: Proficient in Python, C++, and other relevant programming languages for computer vision tasks. Data Handling: Experience working with large datasets, data augmentation, and preprocessing techniques. Optimization: Skills in model optimization techniques such as pruning, quantization, and hardware acceleration (e.g., using GPUs or TPUs). trong working experience in Agile environment - Experience working and understanding of ETL / ELT, Data load process - Knowledge on Cloud Infrastructure and data source integrations - Knowledge on relational Databases - Self-motivated, be able to work independently as well as being a team player - Excellent analytical and problem-solving skills - Ability to handle and respond to multiple stakeholders and queries - Ability to prioritize tasks and update key stakeholders - Strong client service focus and willingness to respond to queries and provide deliverables within prompt timeframes.

Principal Software Engineer – AI Location : Gurgaon (In-Office) Working Days : Monday to Saturday (2nd and 4th Saturdays are working) Working Hours : 10:30 AM – 8:00 PM Experience : 6–10 years of hands-on development in AI/ML systems, with deep experience in shipping production-grade AI products Apply at : careers@darwix.ai Subject Line : Application – Principal Software Engineer – AI – [Your Name] About Darwix AI Darwix AI is India’s fastest-growing GenAI SaaS platform transforming how large sales and CX teams operate across India, MENA, and Southeast Asia. We build deeply integrated conversational intelligence and agent assist tools that enable: Multilingual speech-to-text pipelines Real-time agent coaching AI-powered sales scoring Predictive analytics and nudges CRM and telephony integrations Our clients include leading enterprises like IndiaMart, Bank Dofar, Wakefit, GIVA, and Sobha , and our product is deeply embedded in the daily workflows of field agents, telecallers, and enterprise sales teams. We are backed by top VCs and built by alumni from IIT, IIM, and BITS with deep expertise in real-time AI, enterprise SaaS, and automation. Role Overview We are hiring a Principal Software Engineer – AI to lead the development of advanced AI features in our conversational intelligence suite. This is a high-ownership role that combines software engineering, system design, and AI/ML application delivery. You will work across our GenAI stack—including Whisper, LangChain, LLMs, audio streaming, transcript processing, NLP pipelines, and scoring models—to build robust, scalable, and low-latency AI modules that power real-time user experiences. This is not a research role. You will be building, deploying, and optimizing production-grade AI features used daily by thousands of sales agents and managers across industries. Key Responsibilities 1. AI System Architecture & Development Design, build, and optimize core AI modules such as: Multilingual speech-to-text (Whisper, Deepgram, Google STT) Prompt-based LLM workflows (OpenAI, open-source LLMs) Transcript post-processing: punctuation, speaker diarization, timestamping Real-time trigger logic for call nudges and scoring Build resilient pipelines using Python, FastAPI, Redis, Kafka , and vector databases 2. Production-Grade Deployment Implement GPU/CPU-optimized inference services for latency-sensitive workflows Use caching, batching, asynchronous processing, and message queues to scale real-time use cases Monitor system health, fallback workflows, and logging for ML APIs in live environments 3. ML Workflow Engineering Work with Head of AI to fine-tune, benchmark, and deploy custom models for: Call scoring (tone, compliance, product pitch) Intent recognition and sentiment classification Text summarization and cue generation Build modular services to plug models into end-to-end workflows 4. Integrations with Product Modules Collaborate with frontend, dashboard, and platform teams to serve AI output to users Ensure transcript mapping, trigger visualization, and scoring feedback appear in real-time in the UI Build APIs and event triggers to interface AI systems with CRMs, telephony, WhatsApp, and analytics modules 5. Performance Tuning & Optimization Profile latency and throughput of AI modules under production loads Implement GPU-aware batching, model distillation, or quantization where required Define and track key performance metrics (latency, accuracy, dropout rates) 6. Tech Leadership Mentor junior engineers and review AI system architecture, code, and deployment pipelines Set engineering standards and documentation practices for AI workflows Contribute to planning, retrospectives, and roadmap prioritization What We’re Looking For Technical Skills 6–10 years of backend or AI-focused engineering experience in fast-paced product environments Strong Python fundamentals with experience in FastAPI, Flask , or similar frameworks Proficiency in PyTorch , Transformers , and OpenAI API/LangChain Deep understanding of speech/text pipelines, NLP, and real-time inference Experience deploying LLMs and AI models in production at scale Comfort with PostgreSQL, MongoDB, Redis, Kafka, S3 , and Docker/Kubernetes System Design Experience Ability to design and deploy distributed AI microservices Proven track record of latency optimization, throughput scaling, and high-availability setups Familiarity with GPU orchestration, containerization, CI/CD (GitHub Actions/Jenkins), and monitoring tools Bonus Skills Experience working with multilingual STT models and Indic languages Knowledge of Hugging Face, Weaviate, Pinecone, or vector search infrastructure Prior work on conversational AI, recommendation engines, or real-time coaching systems Exposure to sales/CX intelligence platforms or enterprise B2B SaaS Who You Are A pragmatic builder—you don’t chase perfection but deliver what scales A systems thinker—you see across data flows, bottlenecks, and trade-offs A hands-on leader—you mentor while still writing meaningful code A performance optimizer—you love shaving off latency and memory bottlenecks A product-focused technologist—you think about UX, edge cases, and real-world impact What You’ll Impact Every nudge shown to a sales agent during a live customer call Every transcript that powers a manager’s coaching decision Every scorecard that enables better hiring and training at scale Every dashboard that shows what drives revenue growth for CXOs This role puts you at the intersection of AI, revenue, and impact —what you build is used daily by teams closing millions in sales across India and the Middle East. How to Apply Send your resume to careers@darwix.ai Subject Line: Application – Principal Software Engineer – AI – [Your Name] (Optional): Include a brief note describing one AI system you've built for production—what problem it solved, what stack it used, and what challenges you overcame. If you're ready to lead the AI backbone of enterprise sales , build world-class systems, and drive real-time intelligence at scale— Darwix AI is where you belong.

We are excited to share a fantastic opportunity for the AI Lead/Sr. AI-ML Engineer position at Gritstone Technologies . We believe your skills and experience could be a perfect match for this role, and we would love for you to explore this opportunity with us. Design and implement scalable, high-performance AI/ML architectures with Python tailored for real-time and batch processing use cases. Lead the development of robust, end-to-end AI pipelines, including advanced data preprocessing, feature engineering, model development, and deployment. Define and drive the integration of AI solutions across cloud-native platforms (AWS, Azure, GCP) with optimized cost-performance trade-offs. Architect and deploy multimodal AI systems, leveraging advanced NLP (e.g., LLMs, OpenAI-based customizations, scanned invoice data extraction), computer vision (e.g., inpainting, super-resolution scaling, video-based avatar generation), and generative AI technologies (e.g., video and audio generation). Integrate domain-specific AI solutions, such as reinforcement learning, and self-supervised learning models. Implement distributed training and inferencing pipelines using state-of-the-art frameworks. Drive model optimization through quantization, pruning, sparsity techniques, and mixed-precision training to maximize performance across GPU hardware. Develop scalable solutions using large vision-language models (VLMs) and large language models (LLMs). Define and implement MLOps practices for version control, CI/CD pipelines, and automated model deployment using tools like Kubernetes, Docker, Kubeflow, and FastAPI. Enable seamless integration of databases (SQL Server, MongoDB, NoSQL) with AI workflows. Drive cutting-edge research in AI/ML, including advancements in RLHF, retrieval-augmented generation (RAG), and multimodal knowledge graphs. Experiment with emerging generative technologies, such as diffusion models for video generation and neural audio synthesis. Collaborate with cross-functional stakeholders to deliver AI-driven business solutions aligned with organizational goals. null 5+ years of Experience

Company Size Large-scale / Global Experience Required 4 - 7 years Working Days 6 days/week Office Location Viman Nagar, Pune Role & Responsibilities Deliveries With Respect To Conversational Text AI Platform Build and maintain Conversational Text AI system using state-of-the-art LLM frameworks. Collaborate with product owners and domain experts to build reusable components for business process. Develop core infrastructure and reusable components to support the deployment of conversational AI systems. Work on orchestration, prompt engineering, and LLM-powered integrations. Implement scalable solutions integrated with respective echo systems and enterprise data platforms. Contribute to the design of modular, extensible, enterprise-grade architectures. Fine-tune & optimization for speed, accuracy, performance and maintainability across business units. Contribute to CI/CD automation and maintain operational stability of application services. Generative AI & Model Optimization Fine-tune LLMs/SLMs with proprietary NBFC data. Perform distillation, quantization of LLMs for edge deployment. Evaluate and run LLM/SLM models on local/edge server machines. Self-Learning Frameworks Build self-learning systems that adapt without full retraining (e.g., learn new rejection patterns from calls). Implement lightweight local models to enable real-time learning on the edge. Ideal Candidate Educational Qualifications Educational Background: Bachelor’s or Master’s degree in computer science, Engineering, or a related field. Experience: 4–7 years of experience in Python, Node.JS, JavaScript, HTML/CSS, Redis, Postgres, Azure COSMOS, DevOps, CI/CD with exposure to AI/ML Strong programming skills in languages such as Python, Node.JS, JavaScript, HTML/CSS. Familiarity with Redis, Postgres, Vector Embeddings, Speech-to-Text & Text-to-Speech Services, Azure COSMOS, DevOps, CI/CD, Lang-Chain or Lang-Graph. Experience building with or integrating LLMs for task automation, reasoning, or autonomous workflows Strong understanding of prompt engineering, tool calling, and agent orchestration. Perks, Benefits and Work Culture Our people define our passion and our audacious, incredibly rewarding achievements. Bajaj Finance Limited is one of India’s most diversified Non-banking financial companies, and among Asia’s top 10 Large workplaces. If you have the drive to get ahead, we can help find you an opportunity at any of the 500+ locations we’re present in India. Skills: html/css,cd,learning,tool calling,devops,azure cosmos,azure,python,redis,ci/cd,node.js,css,agent orchestration,cosmos,llm frameworks,ai/ml,automation,ci,postgres,prompt engineering,javascript

Company Size Large-scale / Global Experience Required 3 - 7 years Working Days 6 days/week Office Location Viman Nagar, Pune Role & Responsibilities Agentic AI Platform Delivery Develop and maintain autonomous software agents using modern LLM frameworks. Build reusable components for business process automation. Design agent orchestration, prompt engineering, and LLM integrations. Enable deployment across CRM systems and enterprise data platforms. Generative AI & Model Optimization Fine-tune LLMs/SLMs with proprietary NBFC data. Work on model distillation, quantization, and edge deployment readiness. Self-Learning Systems Create adaptive frameworks that learn from interaction outcomes. Implement lightweight models to support real-time decision-making. Ideal Candidate B.E./B.Tech/M.Tech in Computer Science or related field 4–7 years in AI/ML roles with proficiency in: Languages: Python, Node.JS, JavaScript, React, Java Tools/Frameworks: LangChain, Semantic Kernel, LangGraph, CrewAI Platforms: GCP, MS Foundry, Copilot Studio, BigQuery, Power Apps/BI Agent Tools: Agent Development Kit (ADK), Multi-agent Communication Protocol (MCP) Strong understanding of: Prompt engineering, LLM integration, and orchestration Perks, Benefits and Work Culture Our people define our passion and our audacious, incredibly rewarding achievements. Bajaj Finance Limited is one of India’s most diversified Non-banking financial companies, and among Asia’s top 10 Large workplaces. If you have the drive to get ahead, we can help find you an opportunity at any of the 500+ locations we’re present in India. Skills: copilot studio,java,node.js,javascript,automation,bigquery,react,llm integration,prompt,platforms,agent development,prompt engineering,data,langgraph,power apps/bi,multi-agent communication protocol (mcp),langchain,crewai,gcp,semantic kernel,adk,agent development kit (adk),ms foundry,orchestration,python

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 centre 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. Why join us?  Impactful Work: Play a pivotal role in safeguarding Tanla's assets, data, and reputation in the industry.  Tremendous Growth Opportunities: Be part of a rapidly growing company in the telecom and CPaaS space, with opportunities for professional development.  Innovative Environment: Work alongside a world-class team in a challenging and fun environment, where innovation is celebrated. Tanla is an equal opportunity employer. We champion diversity and are committed to creating an inclusive environment for all employees. www.tanla.com

Experience: 2+ years in AI/ML, with hands-on development & leadership Key Responsibilities: ● Architect, develop, and deploy AI/ML solutions across various business domains. ● Research and implement cutting-edge deep learning, NLP, and computer vision models. ● Optimize AI models for performance, scalability, and real-time inference. ● Develop and manage data pipelines, model training, and inference workflows. ● Integrate AI solutions into microservices and APIs using scalable architectures. ● Lead AI-driven automation and decision-making systems. ● Ensure model monitoring, explainability, and continuous improvement in production. ● Collaborate with data engineering, software development, and DevOps teams. ● Stay updated with LLMs, transformers, federated learning, and AI ethics. ● Mentor AI engineers and drive AI research & development initiatives. Technical Requirements: ● Programming: Python (NumPy, Pandas, Scikit-learn). ● Deep Learning Frameworks: TensorFlow, PyTorch, JAX. ● NLP & LLMs: Hugging Face Transformers, BERT, GPT models, RAG, fine-tuning LLMs. ● Computer Vision: OpenCV, YOLO, Faster R-CNN, Vision Transformers (ViTs). ● Data Engineering: Spark, Dask, Apache Kafka, SQL/NoSQL databases. ● Cloud & MLOps: AWS/GCP/Azure, Kubernetes, Docker, CI/CD for ML pipelines. ● Optimization & Scaling: Model quantization, pruning, knowledge distillation. ● Big Data & Distributed Computing: Ray, Dask, TensorRT, ONNX. ● Security & Ethics: Responsible AI, Bias detection, Model explainability (SHAP, LIME). Preferred Qualifications: ● Experience with real-time AI applications, reinforcement learning, or edge AI. ● Contributions to AI research (publications, open-source contributions). ● Experience integrating AI with ERP, CRM, or enterprise solutions. Job Types: Full-time, Permanent Pay: ₹100,000.00 - ₹500,000.00 per year Schedule: Day shift Application Question(s): What is your current CTC? Experience: AI: 2 years (Required) Machine learning: 2 years (Required) Work Location: In person

About the Company: Aerobotics7 (A7) is a mission-driven deep-tech startup focused on developing an autonomous next-gen sensing and advanced AI platform to detect and identify hidden threats like landmines and UXOs, in real-time. Our driven team is committed to building mission-critical products through continuous learning, rapid execution and close cross-collaboration. We are intentionally a small, strong, and highly technical team to deliver impactful products that can solve some of the biggest problems in the world. What you’ll do: Lead full ML model lifecycle , from research and experiments to implementation and deployment. Build and deploy deep learning models on GCP and edge devices , ensuring real-time inference. Combine multiple sensor inputs into powerful multi-modal ML models . Implement and refine CNNs, Vision Transformers(ViT) , and other architectures. Design sensor-fusion methods for better perception and decision-making. Optimize inference for low-latency , efficient production use. Work closely with software and hardware teams to bring AI into mission-critical systems. Create and scale pipelines for training, validating, and improving models. What you’ll bring: Deep expertise in TensorFlow and PyTorch . Hands-on experience with CNNs, ViTs , and DL architectures. Experience with multi-modal ML and sensor fusion . Cloud deployment skills- GCP preferred . Edge AI know-how (NVIDIA Jetson, TensorRT, OpenVINO). Proficiency in quantization, pruning, and real-time model optimization. Solid computer vision and object detection experience. Ability to work with limited datasets (using VAEs or similar) to generate synthetic data , and experience with annotation and augmentation . Strong coding skills in Python and C++ , with high-performance computing expertise. Nice to have: 2-4 years of relevant experience. MLOps experience- CI/CD , model versioning , monitoring . Knowledge of reinforcement learning . Experience in working in fast-paced startup environments. AI for autonomy, robotics, or UAV systems. Knowledge of embedded systems and hardware acceleration for AI. Benefits: NOTE: THIS ROLE IS UNDER AEROBOTICS7 INVENTIONS PVT. LTD., AN INDIAN ENTITY. IT IS A REMOTE INDIA-BASED ROLE WITH COMPENSATION ALIGNED TO INDIAN MARKET STANDARDS. WHILE OUR PARENT COMPANY IS US-BASED, THIS POSITION IS FOR CANDIDATES RESIDING AND WORKING IN INDIA. Competitive salary and comprehensive benefits package. Future opportunity for equity options in the company. Opportunity to work on impactful, cutting-edge technology in a collaborative startup environment. Professional growth with extensive learning and career development opportunities. Direct contribution to tangible, real-world impact.

Job Summary: We are looking for a highly motivated and skilled Machine Learning Engineer with 1–3 years of experience and a strong interest in Generative AI technologies. The ideal candidate will contribute to the full lifecycle of Generative AI models — from training and optimization to deployment on AWS and inference API development. You will collaborate with a team of engineers and researchers to build cutting-edge AI-powered solutions that have real-world impact. Key Responsibilities: 1. Generative AI Model Development Design, develop, and implement Generative AI models for applications such as text, image, and code generation. Work with large language models (LLMs) or other generative models using frameworks like TensorFlow, PyTorch, and Hugging Face Transformers. Experiment with model architectures, training techniques, and hyperparameter tuning to optimize performance. 2. Model Optimization & Efficiency Apply techniques such as quantization, pruning, and distillation to improve inference speed and reduce resource consumption. Profile and analyze model performance to identify and eliminate bottlenecks. 3. Cloud Deployment on AWS Deploy AI models to AWS using services such as SageMaker, EC2, ECS, or Lambda. Develop scalable and reliable inference pipelines. Use AWS tools for data storage, model management, and monitoring. 4. Inference API Development Design and develop efficient, secure, and scalable RESTful or gRPC APIs to serve deployed AI models. Ensure high availability, maintainability, and performance of APIs. 5. Troubleshooting & Debugging Diagnose and resolve issues related to model performance, deployment, and API functionality. Implement monitoring and logging systems to proactively manage issues. 6. Model Retraining & Continuous Improvement Implement strategies for continuous learning by retraining models with new data and user feedback. Monitor production model performance and trigger retraining workflows as needed. 7. Collaboration & Communication Work closely with ML engineers, data scientists, and software developers to drive project goals. Communicate progress, insights, and challenges effectively with stakeholders. 8. Documentation Create comprehensive documentation for models, training workflows, deployment processes, and APIs. Required Qualifications: Bachelor’s or Master’s degree in Computer Science, Machine Learning, AI, or a related field. 1–3 years of hands-on experience in machine learning model development and deployment. Sound understanding of Generative AI concepts (e.g., Transformers, GANs, Diffusion Models). Experience with at least one deep learning framework: TensorFlow or PyTorch. Proficiency in Python. Solid knowledge of ML algorithms, data preprocessing, and evaluation metrics. Proven experience in deploying models on AWS. Strong skills in developing and consuming RESTful or gRPC APIs. Familiarity with Git or other version control systems. Excellent problem-solving, communication, and teamwork skills. Preferred Qualifications: Experience working with LLMs and the Hugging Face Transformers library. Knowledge of MLOps practices and related tools. Experience with Docker, Kubernetes, or other containerization technologies. Familiarity with data engineering tools and pipelines. Experience with monitoring tools such as CloudWatch, Prometheus, or Grafana. Contributions to open-source projects or notable personal AI projects. Job Type: Full-time Benefits: Health insurance Provident Fund Ability to commute/relocate: Pimple Soudagar, Pune, Maharashtra: Reliably commute or planning to relocate before starting work (Required) Application Question(s): What is your Notice period? Experience: Machine learning: 1 year (Required) Work Location: In person

10+ Experience required only Key Responsibilities Design and implement machine learning models for intelligent document processing. Work on advanced NLP and transformer-based models for high-accuracy data extraction. Develop and maintain workflows for Record of Processing Activities (RoPA), DPIAs, and consent management. Lead AI model development life cycle including data preparation, model training, fine-tuning, and evaluation. Collaborate with cross-functional teams to gather requirements and deploy AI-driven solutions. Participate in platform development roadmaps, release planning, and client interaction. Stay updated with AI trends and contribute to the company’s innovation strategy. Technical Skills Required Proficient in Python for data science and machine learning applications. Strong experience with NLP tools and frameworks like PyTorch or TensorFlow. Hands-on experience with LLMs and model fine-tuning, prompt engineering, LoRA, quantization, etc. Familiarity with tools such as MLflow, Docker, and FastAPI. Experience with building and deploying AI models for document understanding and automation. Preferred Qualifications Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or related field. Experience in developing AI solutions in industries like Financial Services, Healthcare, or Consumer Tech. Prior work with multi-modal AI systems (NLP + Computer Vision) is a plus. Soft Skills Excellent problem-solving and analytical skills. Strong written and verbal communication. Comfortable with client interactions and presentations. Ability to work independently and lead small project teams. Job Type: Full-time Pay: ₹1,200,000.00 - ₹3,000,000.00 per year Schedule: Day shift Fixed shift

Role Overview: We are looking for a skilled Deep Learning Engineer with hands-on experience in object detection and segmentation models . The ideal candidate should be proficient in model development, data preparation, and deployment in real-time environments. Key Responsibilities: Implement and fine-tune object detection models (YOLOvX, Faster R-CNN, EfficientDet, SSD, Mask R-CNN). Work on real-time computer vision applications and performance optimization. Annotate and prepare datasets using Roboflow, CVAT, LabelImg, etc. Collaborate on research and development to enhance model performance and robustness. Deploy models using Docker on Linux/Windows systems; edge deployment experience is a plus. Document code, experiments, and deployment processes; collaborate with cross-functional teams. Required Skills: Strong Python programming skills. Experience with TensorFlow, PyTorch, OpenCV, and ONNX. Hands-on with Docker, Linux/Windows environments. Familiarity with model optimization (quantization, pruning). Experience in edge deployments (e.g., NVIDIA Jetson, TensorRT, OpenVINO) is an advantage. Knowledge of experiment tracking tools like MLflow or Weights & Biases is a plus. Qualifications: Bachelor’s or Master’s in Computer Science, AI, Data Science, or a related field. Strong analytical, problem-solving, and team collaboration skills. Job Type: Full-time Pay: ₹300,000.00 - ₹1,500,000.00 per year Schedule: Day shift Work Location: In person

Responsibilities Anchor ML development track in a client project Data collection, profiling, EDA & data preparation AI Model development, experimentation, tuning & validation Present findings to business users & project management teams Propose ML based solution approaches & estimates for new use cases Contribute to AI based modules in Infosys solutions development Explore new advances in AI continuously and execute PoCs Mentoring – Guide junior team members & evangelize AI in organization Technical skills Programming: Python, R, SQL ML algorithms: Statistical ML algorithms Deep Neural Network architectures Model ensembling Generative AI models ML for Responsible AI AI domains - NLP, speech, computer vision, structured data Learning patterns – supervised, unsupervised, reinforcement learning Tools for data analysis, auto ML, model deployment & scaling, model fine tuning Knowledge of different Model fine tuning approaches for Large models Knowledge of datasets, pre-built models available in open community and 3rd party providers Knowledge of software development & architectures Knowledge of hyperscalers & their AI capabilities – Azure, AWS, GCP Knowledge of Model Quantization and Pruning Past experience playing a Data Scientist role

About The Role We are seeking a talented and motivated Mid-Level Gen AI / AI Engineer to join our team. In this role, you will be responsible for designing, developing, and deploying innovative AI solutions, with a focus on generative AI and RAG architectures. You will work closely with cross-functional teams to bring cutting-edge AI products to market. Key Responsibilities Model Development and Fine-tuning: Develop and fine-tune state-of-the-art generative AI models, such as large language models and image generation models. Implement techniques for model optimization, including quantization, pruning, and knowledge distillation. RAG Architecture: Design and implement robust RAG architectures to enable efficient information retrieval and generation. Integrate diverse data sources and knowledge bases into RAG systems. Optimize query processing and response generation for optimal performance. AI System Development: Build scalable and reliable AI systems, including APIs, microservices, and data pipelines. Collaborate with ML Ops engineers to deploy and manage AI models in production environments. Experimentation and Innovation: Stay up-to-date with the latest advancements in AI research and explore new techniques. Conduct experiments to improve model performance and system efficiency. Contribute to a culture of innovation and continuous learning. Qualifications And Experience Bachelor's degree in Computer Science, Computer Engineering, or a related field. 2+ years of experience in AI/ML, with a focus on generative AI and RAG architectures. Strong programming skills in Python and experience with deep learning frameworks (TensorFlow, PyTorch, etc.). Strong and demonstratable skills in GenAI technologies, like OpenAI, Anthropic, or Llama Hands-on experience with natural language processing (NLP), computer vision, and machine learning techniques. Knowledge of cloud platforms (AWS, Azure, GCP) and their AI/ML services. Excellent communication and problem-solving skills. Skills: llama,cloud platforms (aws, azure, gcp),rag architectures,machine learning,tensorflow,python,pytorch,openai,natural language processing (nlp),models,generative ai,rag,computer vision,anthropic,aws

Responsibilities: Design, develop, and fine-tune transformer-based architectures (e.g., BERT, GPT, LLaMA, T5) for various NLP tasks. Implement reinforcement learning (RL) algorithms such as PPO, A3C, or DQN for optimizing large-scale models. Conduct experiments for model fine-tuning and evaluation , ensuring optimal performance on real-world data. Work on prompt engineering, instruction tuning, and alignment of LLMs using techniques like RLHF. Collaborate with cross-functional teams (product, data science, and engineering) to deliver scalable ML solutions. Research and stay up-to-date with state-of-the-art techniques in transformers, generative AI, and RL. Optimize models for efficiency and deployment , leveraging quantization, pruning, or distillation. Requirements: 3+ years of hands-on experience in machine learning, with a focus on NLP and reinforcement learning. Strong expertise in transformer architectures and libraries such as Hugging Face Transformers, PyTorch, or TensorFlow . Proven experience in fine-tuning large language models (LLMs) and optimizing training pipelines. In-depth understanding of reinforcement learning algorithms and real-world RL applications. Experience with distributed training and large-scale data pipelines. Proficiency in Python and ML frameworks like PyTorch, TensorFlow, or JAX . Strong knowledge of cloud platforms (AWS, GCP, Azure) and ML Ops practices. Advanced degree (Master’s/PhD) in Computer Science, AI, Machine Learning, or a related field.

Responsibilities Anchor ML development track in a client project Data collection, profiling, EDA & data preparation AI Model development, experimentation, tuning & validation Present findings to business users & project management teams Propose ML based solution approaches & estimates for new use cases Contribute to AI based modules in Infosys solutions development Explore new advances in AI continuously and execute PoCs Mentoring – Guide junior team members & evangelize AI in organization Technical skills Programming: Python, R, SQL ML algorithms: Statistical ML algorithms o Deep Neural Network architectures o Model ensembling o Generative AI models o ML for Responsible AI AI domains - NLP, speech, computer vision, structured data Learning patterns – supervised, unsupervised, reinforcement learning Tools for data analysis, auto ML, model deployment & scaling, model fine tuning Knowledge of different Model fine tuning approaches for Large models Knowledge of datasets, pre-built models available in open community and 3rd party providers Knowledge of software development & architectures Knowledge of hyperscalers & their AI capabilities – Azure, AWS, GCP Knowledge of Model Quantization and Pruning

We’re looking for a hands-on backend expert who can take our FastAPI-based platform to the next level: production-grade model-inference services, agentic AI workflows, and seamless integration with third-party LLMs and NLP tooling. Note: This role is being hired for one of our client companies. The company name will be disclosed during the interview process. WHAT YOU'LL BUILD 1. Core Backend Enhancements Build APIs Harden security (OAuth2/JWT, rate-limiting, SecretManager) and observability (structured logging, tracing) Add CI/CD, test automation, ,health checks and SLO dashboards 2. Awesome UI Interfaces React.js/Next.js, Redact/Context, Tailwind / MUI / Custom-CSS / Shadcn / Axios 3. LLM & Agentic Services Design micro/mini-services that host and route to OpenAI, Anthropic, local HF models, embeddings & RAG pipelines Implement autonomous/recursive agents that orchestrate multi-step chains (Tools, Memory, Planning) 4. Model-Inference Infrastructure Spin up GPU / CPU inference servers behind an API gateway Optimize throughput with batching, streaming, quantization & caching (Redis / pgvector) 5. NLP & Data Services Own the NLP stack: Transformers for classification, extraction, and embedding generation. Build data pipelines that join aggregated business metrics with model telemetry for analytics TECH STACK YOU'LL WORK WITH 1.Fullstack/Backend Infrastructure • Python, FastAPI, Starlette, Pydantic • Async SQLAlchemy, Postgres, Alembic, pgvector • Docker, Kubernetes, or ECS/Fargate - AWS (Or) GCP • Redis / RabbitMQ / Celery (jobs & caching) • Prometheus, Grafana, OpenTelemetry • If you are a full-stack person, then - react.js / next.js / shadcn / tailwind.css / MUI 2.AI / NLP • HuggingFace Transformers, LangChain / Llama-Index, Torch / TensorRT • OpenAI, Anthropic, Azure OpenAI, Cohere APIs • Vector search (Pinecone, Qdrant, PGVector) 3. Tooling • Pytest, GitHub Actions • Terraform / CDK preferred MUST HAVE EXPERIENCE • 3+ yrs building production Python REST APIs (FastAPI, Flask, or Django-REST) • SQL schema design & query optimization in Postgres (CTEs, JSONB) • Deep knowledge of async patterns & concurrency (asyncio, AnyIO, celery) • Crafted awesome UI Applications that integrate with the backend API • Hands-on with RAG, LLM/embedding workflows, prompt-engineering & at least one of “agent-ops” frameworks (LangGraph, CrewAI, AutoGen) • Cloud container orchestration (Any of K8s, ECS, GKE, AKS, etc.) • CI/CD pipelines and infra-as-code NICE-TO-HAVE EXPERIENCE • Streaming protocols (Server-Sent Events, WebSockets, gRPC) • NGINX Ingress / AWS API Gateway • RBAC / multi-tenant SaaS security hardening • Data privacy, PII redaction, secure key vault integrations • Bitemporal or event-sourced data models WHY DOES THIS ROLE MATTER? We’re growing fast. Products are live, but evolving. Challenges are real, and the opportunity to own systems end-to-end is massive. You’ll lead how we scale AI services, work directly with the founder, and shape what the next wave of our platform looks like. If you’re looking for meaningful ownership and a chance to work on hard, forward-looking problems, this role is for you.

Job Summary We are looking for a hands-on Data Scientist with deep expertise in NLP and Generative AI to help build and refine the intelligence behind our agentic AI systems. You will be responsible for fine- tuning, prompt engineering, and evaluating LLMs that power our digital workers across real-world workflows. Years of Experience 3 - 6 Years Budget 18 LPA to 24 LPA Location Chennai Immediate to 30 days Key Responsibilities · Fine-tune and evaluate LLMs (e.g., Mistral, LLaMA, Qwen) using frameworks like Unsloth, HuggingFace, and DeepSpeed · Develop high-quality prompts and RAG pipelines for few-shot and zero-shot performance · Analyze and curate domain-specific text datasets for training and evaluation · Conduct performance and safety evaluation of fine-tuned models · Collaborate with engineering teams to integrate models into agentic workflows · Stay up to date with the latest in open-source LLMs and GenAI tools, and rapidly prototype experiments · Apply efficient training and inference techniques (LoRA, QLoRA, quantization, etc.) Requirements · 3+ years of experience in Natural Language Processing (NLP) and machine learning applied to text · Strong coding skills in python · Hands-on experience fine-tuning LLMs (e.g., LLaMA, Mistral, Falcon, Qwen) using frameworks like Unsloth, HuggingFace Transformers, PEFT, LoRA, QLoRA, bitsandbytes · Proficient in PyTorch (preferred) or TensorFlow, with experience in writing custom training/evaluation loops · Experience in dataset preparation, tokenization (e.g., Tokenizer, tokenizers), and formatting for instruction tuning (ChatML, Alpaca, ShareGPT formats) · Familiarity with retrieval-augmented generation (RAG) using FAISS, Chroma, Weaviate, or Qdrant · Strong knowledge of prompt engineering, few-shot/zero-shot learning, chain-of-thought prompting, and function-calling patterns · Exposure to agentic AI frameworks like CrewAI, Phidata, LangChain, LlamaIndex, or AutoGen · Experience with GPU-accelerated training/inference and libraries like DeepSpeed, Accelerate, Flash Attention, Transformers v2, etc. · Solid understanding of LLM evaluation metrics (BLEU, ROUGE, perplexity, pass@k) and safety- related metrics (toxicity, bias) · Ability to work with open-source checkpoints and formats (e.g., safetensors, GGUF, HF Hub, GPTQ, ExLlama) · Comfortable with containerized environments (Docker) and scripting for training pipelines, data curation, or evaluation workflows Nice to Haves · Experience in Linux (Ubuntu) · Terminal/Bash Scripting