25 Tensorrt Jobs

You are an innovation technology company focused on helping companies accelerate their digital initiatives from strategy and planning through execution. Leveraging deep technical expertise, Agile methodologies, and data-driven intelligence, you modernize systems of engagement and simplify human/tech interaction. Amazing things happen in environments where everyone feels a true sense of belonging and has the skills and opportunities to succeed. Investing in talent and supporting career growth is a priority, always looking for amazing talent to contribute to growth by delivering top results for clients. Join the team to challenge yourself and accomplish meaningful work. As a highly experienced Computer Vision Architect with deep expertise in Python, you will design and lead the development of cutting-edge vision-based systems. Architecting scalable solutions leveraging advanced image and video processing, deep learning, and real-time inference, you will collaborate with cross-functional teams to deliver high-performance, production-grade computer vision platforms. Key Responsibilities: - Architect and design end-to-end computer vision solutions for real-world applications like object detection, tracking, OCR, facial recognition, and scene understanding. - Lead R&D initiatives and prototype development using modern CV frameworks such as OpenCV, PyTorch, TensorFlow. - Optimize computer vision models for performance, scalability, and deployment on cloud, edge, or embedded systems. - Define architecture standards and best practices for Python-based CV pipelines. - Collaborate with product teams, data scientists, and ML engineers to translate business requirements into technical solutions. - Stay updated with the latest advancements in computer vision, deep learning, and AI. - Mentor junior developers and contribute to code reviews, design discussions, and technical documentation. Required Skills & Qualifications: - Bachelors or Masters degree in Computer Science, Electrical Engineering, or related field (PhD is a plus). - 8+ years of software development experience, with 5+ years in computer vision and deep learning. - Proficiency in Python and libraries such as OpenCV, NumPy, scikit-image, Pillow. - Experience with deep learning frameworks like PyTorch, TensorFlow, or Keras. - Strong understanding of CNNs, object detection (YOLO, SSD, Faster R-CNN), semantic segmentation, and image classification. - Knowledge of MLOps, model deployment strategies (e.g., ONNX, TensorRT), and containerization (Docker/Kubernetes). - Experience working with video analytics, image annotation tools, and large-scale dataset pipelines. - Familiarity with edge deployment (Jetson, Raspberry Pi, etc.) or cloud AI services (AWS SageMaker, Azure ML, GCP AI).,

As a Lead AI ML Developer at Reverence Technologies, you will be leading a team of machine learning and computer vision engineers to deliver high-quality AI/ML solutions for video analytics projects. In this full-time onsite role, located in Noida & Kochi, you will be responsible for collaborating with cross-functional teams to understand business requirements, develop project plans, and ensure the scalability and maintainability of AI/ML solutions. With over 10 years of experience, you will leverage your expertise in developing and deploying machine learning models to guide the team towards successful project outcomes. Your role will involve staying updated with the latest AI/ML research and technologies to evaluate their impact on business operations, in addition to managing team performance, providing mentorship, and fostering a positive team culture. To qualify for this position, you should have a minimum of 6 years of experience in developing and deploying machine learning models, along with at least 3 years of experience in leading machine learning teams. Strong programming skills in C++ and other relevant languages are essential, as well as familiarity with machine learning libraries such as OpenCV, OpenVino, TensorFlow, PyTorch, etc. Experience with cloud platforms like AWS, GCP, or Azure will be advantageous, along with excellent communication and interpersonal skills to collaborate effectively with cross-functional teams.,

Job Title: AI Research Engineer Intern (Fresher) Reporting to: Lead Research & Innovation Lab Location: remote/ Hybrid (Chennai, India) Engagement: 6-month, full-time paid internship with pre-placement-offer track 1. Why this role exists Stratsyn AI Technology Services is turbo-charging Stratsyns cloud-native Enterprise Intelligence & Management Suite a modular SaaS ecosystem that fuses advanced AI, low-code automation, multimodal search, and next-generation Virtual workforce agents. The platform unifies strategic planning, document intelligence, workflow orchestration, and real-time analytics, empowering C-suite leaders to simulate scenarios, orchestrate execution, and convert insight into action with unmatched speed and scalability. To keep pushing that frontier, we need sharp, curious minds who can translate cutting-edge research into production-grade capabilities for this suite. This internship is our talent-funnel into future Research Engineer and Product Scientist roles. 2. What youll do (core responsibilities) % FocusKey Responsibility 30 %Rapid Prototyping & Experimentation implement state-of-the-art papers (LLMs, graph learning, causal inference, agents), design ablation studies, benchmark against baselines, and iterate fast. 25 %Data Engineering for Research build reproducible datasets, craft synthetic data when needed, automate ETL pipelines, and enforce experiment tracking (MLflow / Weights & Biases). 20 %Model Evaluation & Explainability create evaluation harnesses (BLEU, ROUGE, MAPE, custom KPIs), visualize error landscapes, and generate executive-ready insights. 15 %Collaboration & Documentation author tech memos, well-annotated notebooks, and contribute to internal knowledge bases; present findings in weekly research stand-ups. 10 %Innovation Scouting scan arXiv, ACL, NeurIPS, ICML, and startup ecosystems; summarize high-impact research and propose areas for IP creation within the Suite. 3. What you will learn / outcomes to achieve Master the end-to-end research workflow: literature review ? hypothesis ? prototype ? validation ? deployment shadow. Deliver one peer-review-quality technical report and two production-grade proof-of-concepts for the Suite. Achieve a measurable impact (e.g., 8-10 % forecasting-accuracy lift or 30 % latency reduction) on a live micro-service. 4. Minimum qualifications (freshers welcome) B.E./B.Tech/M.Sc./M.Tech in CS, Data Science, Statistics, EE, or related (2024-2026 pass-out). Fluency in Python and at least one deep-learning framework (PyTorch preferred). Solid grasp of linear algebra, probability, optimization, and algorithms. Hands-on academic or personal projects in NLP, CV, time-series, or RL (GitHub links highly valued). 5. Preferred extras Publications or Kaggle/ML-competition record. Experience with distributed training (GPU clusters, Ray, Lightning) and experiment-tracking tools. Familiarity with MLOps (Docker, CI/CD, Kubernetes) or data-centric AI. Domain knowledge in supply-chain, fintech, climate, or marketing analytics. 6. Key attributes & soft skills First-principles thinker questions assumptions, proposes novel solutions. Bias for action prototypes in hours, not weeks; embraces agile experimentation. Storytelling ability explains complex models in clear, executive-friendly language. Ownership mentality treats the prototype as a product, not just a demo. 7. Tech stack youll touch Python | PyTorch | Hugging Face | TensorRT | LangChain | Neo4j/GraphDB | PostgreSQL | Airflow | MLflow | Weights & Biases | Docker | GitHub Actions | JAX (exploratory) 8. Internship logistics & perks Competitive monthly stipend + performance bonus. High-end workstation + GPU credits on our private cloud. Dedicated mentor and 30-60-90-day learning plan. Access to premium research portals and paid conference passes. Culture of radical candor, weekly brown-bag tech talks, and hack days. Fast-track to full-time AI Research Engineer upon successful completion. 9. Application process Apply via email: Send rsum, brief statement of purpose, and GitHub/portfolio links to [HIDDEN TEXT] . Online coding assessment: algorithmic + ML fundamentals. Technical interview (2 rounds): deep dive into projects, math, and research reasoning. Culture-fit discussion: with Research Lead & CPO. Offer & onboarding target turnaround < 3 weeks. Show more Show less

As a Computer Vision Engineer at wTVision in Noida, India, you will be an integral part of our development team, focusing on designing and implementing cutting-edge computer vision algorithms and systems. Your role will involve optimizing machine learning algorithms, conducting spatial and temporal analysis of object movements, developing real-time processing algorithms for Vision-AI perception metadata, and collaborating with the product team to define new features and functionalities. Your responsibilities will include analyzing and enhancing the end-to-end accuracy of the computer vision pipeline, staying abreast of emerging technologies, working on scalable software solutions with the software development team, and deploying models on edge devices like NVIDIA Jetson. You will be expected to have a strong academic background in Computer Science or related fields, proficiency in programming languages such as Python and C++, experience in image and video processing, and excellent communication and collaboration skills. Ideal candidates should have hands-on expertise with PyTorch, TensorRT, CuDNN, and Deep Learning frameworks like TensorFlow and Keras. A solid understanding of object-oriented programming, parallel computing, and concepts like Linear Algebra and 3D Geometry is required. Additionally, experience with NVIDIA Deep Stream, Jetson, and CUDA programming, knowledge of cloud platforms such as AWS, GCP, or Azure, familiarity with containerization technologies, and contributions to the computer vision community are considered advantageous. If you are self-motivated, possess strong problem-solving skills, and can deliver high-quality work while handling multiple tasks, we encourage you to apply. A passion for sports and technology will be a plus in this dynamic and innovative work environment.,

We are looking for a Director of Engineering (AI Systems & Secure Platforms) to join our client&aposs Core Engineering team at Thane (Maharashtra India). The ideal candidate should have 1215+ years of experience in architecting and deploying AI systems at scale, with deep expertise in agentic AI workflows, LLMs, RAG, Computer Vision, and secure mobile/wearable platforms. Top 3 Daily Tasks: ? Architect, optimize, and deploy LLMs, RAG pipelines, and Computer Vision models for smart glasses and other edge devices. ? Design and orchestrate agentic AI workflowsenabling autonomous agents with planning, tool usage, error handling, and closed feedback loops. ? Collaborate across AI, Firmware, Security, Mobile, Product, and Design teams to embed invisible intelligence within secure wearable systems. Must have 1215+ years of experience in Applied AI, Deep Learning, Edge AI deployment, Secure Mobile Systems, and Agentic AI Architecture. Must have: -Programming languages: Python, C/C++, Java (Android), Kotlin, JavaScript/Node.js, Swift, Objective-C, CUDA, Shell scripting -Expert in TensorFlow, PyTorch, ONNX, HuggingFace; model optimization with TensorRT, TFLite -Deep experience with LLMs, RAG pipelines, vector DBs (FAISS, Milvus) -Proficient in agentic AI workflowsmulti-agent orchestration, planning, feedback loops -Strong in privacy-preserving AI (federated learning, differential privacy) -Secure real-time comms (WebRTC, SIP, RTP) Nice to have: -Experience with MCP or similar protocol frameworks -Background in wearables/XR or smart glass AI platforms -Expertise in platform security architectures (sandboxing, auditability) Industry Technology, Information and Internet Employment Type Full-time Show more Show less

computer vision or deep learning roles industrial/safety inspection datasets (e.g., PPE detection, visual defect classification). Familiarity with MLOps tools like MLflow, DVC, or ClearML. ONNX, TensorRT, OpenVINO

As an AI/ML Lead specializing in Facial Recognition & Video Intelligence at Live Eye Surveillance, your primary responsibility will be to drive the development of advanced features for our AI-powered Video Management Software (VMS) platform. You will play a crucial role in leading the research, creation, and optimization of AI models that enhance real-time monitoring and security operations using IP camera feeds. Additionally, you will oversee a team of AI/ML engineers, collaborating with cross-functional teams to ensure the seamless integration of cutting-edge AI modules into our surveillance technology. Your contributions will directly impact the efficiency, accuracy, and scalability of our security solutions across various business environments. To excel in this role, you should possess at least 3 years of practical experience in Machine Learning and Deep Learning, with a focus on Computer Vision. Proficiency in Python, TensorFlow/PyTorch, OpenCV, and other relevant deep learning libraries is essential for creating robust models for facial recognition and object detection. Your expertise in optimizing AI pipelines for real-time performance and compatibility with edge devices will be critical for ensuring the effectiveness of our surveillance systems. Furthermore, your ability to lead, mentor, and inspire a team of AI/ML engineers, combined with strong communication and problem-solving skills, will be key assets in driving the success of our AI initiatives. Ideally, you should hold a Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Experience with optimization tools such as ONNX, TensorRT, and familiarity with integrating AI models with IP camera feeds using RTSP/ONVIF protocols will be advantageous. Keeping abreast of the latest advancements in deep learning and computer vision research is essential to stay ahead in this rapidly evolving field. Additionally, any background in surveillance systems, familiarity with cloud platforms like AWS, Azure, or GCP, and experience with deploying ML models using Docker, Git, and CI/CD pipelines would be beneficial. Joining Live Eye Surveillance offers a unique opportunity to lead in a dynamic environment and contribute to the development of innovative security technologies. You will have the chance to work on groundbreaking projects, collaborate with talented teams, and make a tangible impact on global security deployments. At Live Eye, we offer a competitive compensation package, a flexible hybrid work setup, and the potential for rapid career growth. If you are passionate about leveraging AI and ML to enhance security solutions, we invite you to send your resume to careers@myliveeye.com and explore the exciting opportunities available at www.myliveeye.com.,

Yubi, formerly known as CredAvenue, is re-defining global debt markets by freeing the flow of finance between borrowers, lenders, and investors. We are the world's possibility platform for the discovery, investment, fulfillment, and collection of any debt solution. At Yubi, opportunities are plenty and we equip you with tools to seize it. In March 2022, we became India's fastest fintech and most impactful startup to join the unicorn club with a Series B fundraising round of $137 million. In 2020, we began our journey with a vision of transforming and deepening the global institutional debt market through technology. Our two-sided debt marketplace helps institutional and HNI investors find the widest network of corporate borrowers and debt products on one side and helps corporates to discover investors and access debt capital efficiently on the other side. Switching between platforms is easy, which means investors can lend, invest and trade bonds - all in one place. All of our platforms shake up the traditional debt ecosystem and offer new ways of digital finance. Yubi Credit Marketplace - With the largest selection of lenders on one platform, our credit marketplace helps enterprises partner with lenders of their choice for any and all capital requirements. Yubi Invest - Fixed income securities platform for wealth managers & financial advisors to channel client investments in fixed income Financial Services Platform - Designed for financial institutions to manage co-lending partnerships & asset-based securitization Spocto - Debt recovery & risk mitigation platform Corpository - Dedicated SaaS solutions platform powered by Decision-grade data, Analytics, Pattern Identifications, Early Warning Signals, and Predictions to Lenders, Investors, and Business Enterprises So far, we have onboarded over 17000+ enterprises, 6200+ investors & lenders and have facilitated debt volumes of over INR 1,40,000 crore. Backed by marquee investors like Insight Partners, B Capital Group, Dragoneer, Sequoia Capital, LightSpeed, and Lightrock, we are the only-of-its-kind debt platform globally, revolutionizing the segment. At Yubi, People are at the core of the business and our most valuable assets. Yubi is constantly growing, with 1000+ like-minded individuals today, who are changing the way people perceive debt. We are a fun bunch who are highly motivated and driven to create a purposeful impact. Come, join the club to be a part of our epic growth story. This particular role is within our Yubi Invest vertical, and you would get to work on building our bonds platform, called Aspero, for retail users. Be able to operate in ambiguous situations and define clear objectives by breaking down the narratives independently. Work closely with business, research, data and engineering teams to understand the user goals, market dynamics and ship products. Aligning product strategy, proposition and roadmap with measurable metrics with all stakeholders. Drive PRDs, product planning, and product design of new features and enhancements. Clearly communicate product and platform benefits to our users and internal stakeholders. We're looking for a highly skilled, results-driven AI engineer who thrives in fast-paced, high-impact environments. If you are passionate about pushing the boundaries of Computer Vision, OCR, and Large Language Models (LLMs) and have a strong foundation in building and deploying AI solutions, this role is for you. As a Senior Data Scientist, you will take ownership of designing and implementing state-of-the-art OCR and Computer Vision systems. This role demands deep technical expertise, the ability to work autonomously, and a mindset that embraces complex challenges head-on. Here, you won't just fine-tune pre-trained modelsyou'll be architecting, optimizing, and scaling AI solutions that power real-world applications. Key Responsibilities: - Architect, develop, and deploy high-performance Computer Vision and OCR models for real-world applications. - Implement and optimize state-of-the-art OCR models such as Donut, TrOCR, LayoutLM, and DocFormer for document processing and information extraction. - Fine-tune and integrate LLMs (GPT, LLaMA, Mistral, etc.) to enhance text understanding and automation. - Develop custom deep learning models for large-scale image and document processing. - Build and optimize end-to-end AI pipelines, ensuring efficient data processing and model deployment. - Work closely with engineers to operationalize AI models in production (Docker, FastAPI, TensorRT, ONNX). - Enhance GPU performance and model inference efficiency, applying techniques such as quantization and pruning. - Stay ahead of industry advancements, continuously experimenting with new AI architectures and training techniques. - Work in a highly dynamic, startup-like environment, balancing rapid experimentation with production-grade robustness. Requirements: - 5-10 years experience - Proven technical expertise - Strong programming skills in Python, PyTorch, TensorFlow with deep experience in Computer Vision and OCR. - Hands-on experience in developing, training, and deploying OCR and document AI models. - Deep understanding of Transformer-based architectures for vision and text processing. - Experience working with Hugging Face, OpenCV, TensorRT, and NVIDIA GPUs for model acceleration. - Autonomous problem solver - Strong experience in scaling AI solutions, including model optimization and deployment on cloud platforms (AWS/GCP/Azure). - Thrives in fast-paced environments - Familiarity with MLOps tools (Docker, FastAPI, Kubernetes) for seamless model deployment. - Experience in multi-modal models (Vision + Text). Nice to Have: - Strong background in vector databases, RAG pipelines, and fine-tuning LLMs for document intelligence. - Contributions to open-source AI projects.,

Visionify is dedicated to leveraging the potential of Computer Vision and AI for various real-world applications. We are currently seeking a highly skilled, motivated, and enthusiastic Senior Computer Vision Engineer to play a crucial role in implementing our strategic plans. As a Senior Computer Vision Engineer at Visionify, you will be tasked with tackling cutting-edge challenges in the realm of Computer Vision by devising innovative algorithms and optimizations. The majority of our projects revolve around practical applications of Computer Vision, necessitating a strong grasp of contemporary model types such as Classification, Object detection, Object Recognition, OCR, LayoutML, and GAN networks. Proficiency in Pytorch is essential for this role, as it serves as our primary programming language. Familiarity with Azure and Azure ML Studio would be advantageous. Candidates applying for this position should remain abreast of the latest advancements and actively contribute to enhancing the Pytorch project's performance and accuracy. Your expertise in PyTorch and its underlying mechanisms will be pivotal in resolving customer challenges and offering valuable insights into product improvements. Experience in optimizing and streamlining models for deployment on edge devices, as well as converting models to NVIDIA TensorRT, will be highly valued. A strong foundation in Python programming is indispensable, given its widespread use in our organization for developing training and inference pipelines. Effective communication and presentation skills are also crucial. The ideal candidate will exhibit a deep passion for artificial intelligence and a commitment to staying updated on industry trends. **Responsibilities:** - Understanding business objectives and devising Computer Vision solutions that align with these goals, including developing training and inference frameworks and leveraging various ML technologies. - Building and optimizing Pytorch models for different runtime environments, including NVIDIA Jetson TensorRT. - Guiding the development team, addressing their queries, and facilitating the timely completion of their tasks. - Creating ML/Computer Vision algorithms to address specific challenges. - Analyzing and visualizing data to identify potential performance-affecting disparities in data distribution, especially when deploying models in real-world scenarios. - Establishing processes for core team operations, such as data acquisition, model training, and prototype development. - Identifying and utilizing open-source datasets for prototype building. - Developing pipelines for data processing, augmentation, training, inference, and active retraining. - Training models, fine-tuning hyperparameters, and devising strategies to address model errors. - Deploying models for production use. **Requirements:** - Bachelor's or Master's degree in Computer Science, Computer Engineering, IT, or a related field. - Minimum of 5 years of relevant experience; candidates with exceptional skills but less experience are encouraged to apply. - Industry experience in Image & Video Processing, including familiarity with OpenCV, GStreamer, TensorFlow, PyTorch, TensorRT, and various model training/inference techniques. - Proficiency in deep learning classification models (e.g., ResNet, Inception, VGG) and object detection models (e.g., MobileNetSSD, Yolo, FastRCNN, MaskRCNN). - Strong command of Pytorch, Torchvision, and the ability to develop training routines and update models effectively. - Familiarity with Colab, Jupyter Notebook, CUDA/GPU, and CNN visualization techniques like CAM and GradCAM. - Expertise in Computer Vision and real-time video processing methods. - Proficient in Python programming and adept at writing reusable code. - Experience with OpenCV, Scikit packages, NVIDIA platform tools (e.g., Deepstream, TensorRT), Python web frameworks (e.g., Flask, Django, FastAPI), and ML platforms (e.g., PyTorch, TensorFlow). - Knowledge of AWS SageMaker, various databases (e.g., Elasticsearch, SQL, NoSQL, Hive), cloud environments (preferably AWS) for software development, GPU-based training infrastructures, Docker, and DevOps and MLOps best practices for ML systems. **Desired Traits:** - Collaborative mindset and ability to thrive in a team environment. - Adaptability to evolving requirements. - Proclivity for innovative problem-solving. - Strong focus on work quality and developing robust code.,

The primary responsibility of this role is to design, develop, and implement cutting-edge image and video generation systems leveraging deep learning models. You will take the lead in exploring and prototyping diffusion, GAN, and transformer-based architectures for generative tasks. Your expertise will be instrumental in optimizing models for quality, speed, and scalability through accelerated compute technologies such as CUDA and TensorRT. Collaboration with cross-functional teams including Product, Design, and Frontend will be essential to seamlessly integrate AI pipelines into production applications and platforms. Additionally, you will play a key role in contributing to system architecture, ensuring reproducibility, versioning, and model evaluation, while also staying updated on the latest advancements in generative AI to facilitate the transition from research and development to production. To excel in this role, you should possess a minimum of 2 years of hands-on experience in the field of AI/ML with a strong emphasis on generative models. Your track record should include practical experience with video generation models like Sora, Gen-2 by Runway, Synthesia, or custom pipelines. A solid background in image generation using Diffusion Models (e.g., Stable Diffusion, DALLE, Imagen) or GANs (e.g., StyleGAN2/3) is essential. Proficiency in Python and deep learning libraries such as PyTorch, TensorFlow, or JAX is required, along with experience in training large-scale models using multi-GPU setups like DDP, DeepSpeed, or Hugging Face Accelerate. A sound understanding of computer vision, image processing, and neural rendering techniques is crucial, as well as practical skills in model fine-tuning and related methodologies like LoRA/PEFT, ControlNet, DreamBooth, and others. Preferred tools and frameworks for this role include Stable Diffusion, DALLE, MidJourney, Sora, Gen-2, VQ-GAN, Pix2Pix, CycleGAN, AnimateDiff, ControlNet, T2I-Adapter, VideoCrafter, Pika Labs, ZeroScope, and ModelScope. Proficiency in FastAPI, Flask, or gRPC for model serving and Streamlit, Gradio, or React for rapid prototyping is advantageous. Experience with cloud platforms such as AWS, GCP, or Azure, particularly with GPU instances, and serving models using TorchServe, NVIDIA Triton, or Vertex AI, will be beneficial in ensuring scalable model deployment. This is a full-time position with a flexible schedule and a day shift from Monday to Friday. The ideal candidate will have a minimum of 2 years of experience in machine learning. The work location is in person, and the expected start date is 01/08/2025.,

You are seeking a highly motivated AI/ML Team Lead to lead a team of machine learning and computer vision engineers for a video analytics project. The ideal candidate should have a strong background in developing and deploying machine learning models, with a proven track record of successfully leading teams in AI/ML projects for video analytics. Your responsibilities will include leading the team to deliver high-quality AI/ML solutions, collaborating with cross-functional teams to identify business requirements, developing and maintaining project plans, ensuring scalability and adherence to best practices, staying updated with the latest AI/ML research, and managing team performance while fostering a positive team culture. The desired candidate should have at least 6 years of experience in developing and deploying machine learning models, along with a minimum of 3 years of experience in leading a team of machine learning engineers. Strong programming skills in C++ and another relevant language are required. Additionally, experience with machine learning libraries and SDKs such as OpenCV, OpenVino, TensorRT, TensorFlow, PyTorch, NVIDIA Deepstream SDK, and familiarity with cloud platforms like AWS, GCP, or Azure are essential. Excellent communication and interpersonal skills are necessary to collaborate effectively with cross-functional teams. If you are passionate about AI/ML, have a successful track record in leading teams for project delivery, and are looking to work in a dynamic and innovative environment, we encourage you to apply for this full-time position in the Software Development department.,

AI/ML Engineer (2-3 positions) Job Summary: We are seeking a highly skilled and motivated AI/ML Engineer with a specialization in Computer Vision & Un-Supervised Learning to join our growing team. You will be responsible for building, optimizing, and deploying advanced video analytics solutions for smart surveillance applications, including real-time detection, facial recognition, and activity analysis. This role combines the core competencies of AI/ML modelling with the practical skills required to deploy and scale models in real-world production environments, both in the cloud and on edge devices. Key Responsibilities: AI/ML Development & Computer Vision Design, train, and evaluate models for: o Face detection and recognition o Object/person detection and tracking o Intrusion and anomaly detection o Human activity or pose recognition/estimation Work with models such as YOLOv8, DeepSORT, RetinaNet, Faster-RCNN, and InsightFace. Perform data preprocessing, augmentation, and annotation using tools like LabelImg, CVAT, or custom pipelines. Surveillance System Integration Integrate computer vision models with live CCTV/RTSP streams for real-time analytics. Develop components for motion detection, zone-based event alerts, person re-identification, and multi-camera coordination. Optimize solutions for low-latency inference on edge devices (Jetson Nano, Xavier, Intel Movidius, Coral TPU). Model Optimization & Deployment Convert and optimize trained models using ONNX, TensorRT, or OpenVINO for real-time inference. Build and deploy APIs using FastAPI, Flask, or TorchServe. Package applications using Docker and orchestrate deployments with Kubernetes. Automate model deployment workflows using CI/CD pipelines (GitHub Actions, Jenkins). Monitor model performance in production using Prometheus, Grafana, and log management tools. Manage model versioning, rollback strategies, and experiment tracking using MLflow or DVC. As an AI/ML Engineer, you should be well-versed of AI agent development and finetuning experience Collaboration & Documentation Work closely with backend developers, hardware engineers, and DevOps teams. Maintain clear documentation of ML pipelines, training results, and deployment practices. Stay current with emerging research and innovations in AI vision and MLOps. Required Qualifications: Bachelors or masters degree in computer science, Artificial Intelligence, Data Science, or a related field. 3-6 years of experience in AI/ML, with a strong portfolio in computer vision, Machine Learning. Hands-on experience with: o Deep learning frameworks: PyTorch, TensorFlow o Image/video processing: OpenCV, NumPy o Detection and tracking frameworks: YOLOv8, DeepSORT, RetinaNet. Solid understanding of deep learning architectures (CNNs, Transformers, Siamese Networks). Proven experience with real-time model deployment on cloud or edge environments. Strong Python programming skills and familiarity with Git, REST APIs, and DevOps tools. Preferred Qualifications: Experience with multi-camera synchronization and NVR/DVR systems. Familiarity with ONVIF protocols and camera SDKs. Experience deploying AI models on Jetson Nano/Xavier, Intel NCS2, or Coral Edge TPU. Background in face recognition systems (e.g., InsightFace, FaceNet, Dlib). Understanding of security protocols and compliance in surveillance systems. Tools & Technologies: Category Tools & Frameworks Languages & AI Python, PyTorch, TensorFlow, OpenCV, NumPy, Scikit-learn Model Serving FastAPI, Flask, TorchServe, TensorFlow Serving, REST/gRPC APIs Model Optimization ONNX, TensorRT, OpenVINO, Pruning, Quantization Deployment Docker, Kubernetes, Gunicorn, MLflow, DVC CI/CD & DevOps GitHub Actions, Jenkins, GitLab CI Cloud & Edge AWS SageMaker, Azure ML, GCP AI Platform, Jetson, Movidius, Coral TPU Monitoring Prometheus, Grafana, ELK Stack, Sentry Annotation Tools LabelImg, CVAT, Supervisely

We are looking for an Embedded AI Software Engineer with a strong background in software development for resource-constrained edge hardware. In this role, you will play a crucial part in creating optimized pipelines that utilize media encoders/decoders, hardware accelerators, and AI inference runtimes on platforms such as NVIDIA Jetson, Hailo, and other edge AI SoCs. Your primary responsibility will involve designing highly efficient, low-latency modules that can operate on embedded devices, requiring deep integration with NVIDIA SDKs like Jetson Multimedia, DeepStream, and TensorRT, as well as broader GStreamer pipelines. Responsibilities include: - Implementing hardware-accelerated video processing pipelines using GStreamer, V4L2, and custom media backends. - Integrating AI inference engines utilizing NVIDIA TensorRT, DeepStream SDK, or similar frameworks such as ONNX Runtime or OpenVINO. - Profiling and optimizing model loading, preprocessing, postprocessing, and buffer management for edge runtime. You will also design software within strict memory, compute, and power constraints specific to edge hardware, leveraging multimedia capabilities and implementing fallback logic for error handling in live deployment scenarios. Additionally, collaborating with kernel modules, device drivers, and board support packages to enhance performance will be a crucial part of your role. Requirements: - Bachelor's or Master's degree in Computer Engineering, Electronics, Embedded Systems, or related fields. - 2-4 years of hands-on experience in developing for edge/embedded systems using C++. - Proficiency in C++11/14/17, multi-threaded programming, video codecs, media IO pipelines, and encoder/decoder frameworks. - Familiarity with GStreamer, V4L2, multimedia buffer handling, TensorRT, DeepStream, CUDA, and NVIDIA's multimedia APIs. - Exposure to runtimes like HailoRT, OpenVINO, or Coral Edge TPU SDK is a plus. Bonus skills include familiarity with build systems like CMake, Bazel, cross-compilation, Yocto, AI model quantization, batching, layer fusion, camera bring-up, video streaming, and live feed inference. To apply for this position, please submit your resume and portfolio details to hire@condor-ai.com with the subject line "Application: Embedded AI Software Engineer." Condor AI is an AI engineering company specializing in deploying artificial intelligence solutions in real-world scenarios. We focus on Edge AI, combining custom hardware with optimized software for fast, reliable on-device intelligence. With expertise in smart cities, industrial automation, logistics, and security, our team brings over a decade of experience in AI, embedded systems, and enterprise-grade solutions. We operate globally, aiming for lean operations and building solutions for production from system design to scaled deployment.,

Ready to shape the future of work At Genpact, we don&rsquot just adapt to change&mdashwe drive it. AI and digital innovation are redefining industries, and we&rsquore leading the charge. Genpact&rsquos , our industry-first accelerator, is an example of how we&rsquore scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. From large-scale models to , our breakthrough solutions tackle companies most complex challenges. If you thrive in a fast-moving, tech-driven environment, love solving real-world problems, and want to be part of a team that&rsquos shaping the future, this is your moment. Genpact (NYSE: G) is an advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions - we help companies across industries get ahead and stay ahead. Powered by curiosity, courage, and innovation , our teams implement data, technology, and AI to create tomorrow, today. Get to know us at and on , , , and . Inviting applications for the role of Lead Consultant - ML/CV Ops Engineer ! We are seeking a highly skilled ML CV Ops Engineer to join our AI Engineering team. This role is focused on operationalizing Computer Vision models&mdashensuring they are efficiently trained, deployed, monitored , and retrained across scalable infrastructure or edge environments. The ideal candidate has deep technical knowledge of ML infrastructure, DevOps practices, and hands-on experience with CV pipelines in production. You&rsquoll work closely with data scientists, DevOps, and software engineers to ensure computer vision models are robust, secure, and production-ready always. Key Responsibilities: End-to-End Pipeline Automation: Build and maintain ML pipelines for computer vision tasks (data ingestion, preprocessing, model training, evaluation, inference). Use tools like MLflow , Kubeflow, DVC, and Airflow to automate workflows. Model Deployment & Serving: Package and deploy CV models using Docker and orchestration platforms like Kubernetes. Use model-serving frameworks (TensorFlow Serving, TorchServe , Triton Inference Server) to enable real-time and batch inference. Monitoring & Observability: Set up model monitoring to detect drift, latency spikes, and performance degradation. Integrate custom metrics and dashboards using Prometheus, Grafana, and similar tools. Model Optimization: Convert and optimize models using ONNX, TensorRT , or OpenVINO for performance and edge deployment. Implement quantization, pruning, and benchmarking pipelines. Edge AI Enablement (Optional but Valuable): Deploy models on edge devices (e.g., NVIDIA Jetson, Coral, Raspberry Pi) and manage updates and logs remotely. Collaboration & Support: Partner with Data Scientists to productionize experiments and guide model selection based on deployment constraints. Work with DevOps to integrate ML models into CI/CD pipelines and cloud-native architecture. Qualifications we seek in you! Minimum Qualifications Bachelor&rsquos or Master&rsquos in Computer Science , Engineering, or a related field. Sound experience in ML engineering, with significant work in computer vision and model operations. Strong coding skills in Python and familiarity with scripting for automation. Hands-on experience with PyTorch , TensorFlow, OpenCV, and model lifecycle tools like MLflow , DVC, or SageMaker. Solid understanding of containerization and orchestration (Docker, Kubernetes). Experience with cloud services (AWS/GCP/Azure) for model deployment and storage. Preferred Qualifications: Experience with real-time video analytics or image-based inference systems. Knowledge of MLOps best practices (model registries, lineage, versioning). Familiarity with edge AI deployment and acceleration toolkits (e.g., TensorRT , DeepStream ). Exposure to CI/CD pipelines and modern DevOps tooling (Jenkins, GitLab CI, ArgoCD ). Contributions to open-source ML/CV tooling or experience with labeling workflows (CVAT, Label Studio). Why join Genpact Be a transformation leader - Work at the cutting edge of AI, automation, and digital innovation Make an impact - Drive change for global enterprises and solve business challenges that matter Accelerate your career - Get hands-on experience, mentorship, and continuous learning opportunities Work with the best - Join 140,000+ bold thinkers and problem-solvers who push boundaries every day Thrive in a values-driven culture - Our courage, curiosity, and incisiveness - built on a foundation of integrity and inclusion - allow your ideas to fuel progress Come join the tech shapers and growth makers at Genpact and take your career in the only direction that matters: Up. Let&rsquos build tomorrow together. Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color , religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. Genpact is committed to creating a dynamic work environment that values respect and integrity, customer focus, and innovation. Furthermore, please do note that Genpact does not charge fees to process job applications and applicants are not required to pay to participate in our hiring process in any other way. Examples of such scams include purchasing a %27starter kit,%27 paying to apply, or purchasing equipment or training.

Role & responsibilities : Job Title / Designation: Solution Architect/Project Manager/Associate Director based on experience & expertise Business Unit : Embedded Engineering Services (EES) Industry Experience Range : 8+ years Job Location : Preferably Pune / Ahmedabad / Bangalore Shift : General Shift (Mon-Fri) Job Function, Roles & Responsibilities: Lead strategic initiatives and own the practice for Edge AI/ML, data pipelines, and intelligent embedded systems Define and build the competency roadmap for machine learning, deep learning, model deployment, and real-time inferencing on edge platforms Oversee data creation including data collection, dataset curation, annotation, cleaning, augmentation, and synthetic data generation Champion use cases involving sensor fusion, combining data from multiple sources (vision, IMU, radar, audio, etc.) to create robust, efficient, and context-aware edge intelligence solutions Drive edge analytics and on-device learning across verticals such as Industrial Automation, Medical Devices, Automotive, and Smart Consumer Electronics Collaborate with global customers to gather requirements, architect solutions, track project delivery, and ensure alignment with business objectives Support business development with presales solutioning, proposal writing, and effort estimation Drive internal capability building through mentoring, training, and competency development Preferred candidate profile: ________________________________________ Experience: 8+ years in embedded systems, AI/ML, and data engineering, with a strong focus on edge intelligence and real-time systems. At least 3 years in a technical leadership or strategic role. Prior experience in a product engineering services environment preferred. ________________________________________ Area of Expertise: Proven expertise in deploying ML/DL models on edge devices (NVIDIA Jetson, NXP i.MX, Qualcomm QCS, TI Sitara, etc.) Strong knowledge of data workflows: dataset generation, manual/automated annotation, data cleaning, augmentation, and synthetic data creation Deep understanding of sensor fusion techniques combining inputs from vision, audio, IMU, radar, LIDAR, and other sources to improve model accuracy and efficiency Experience in model optimization using TensorRT, ONNX, OpenVINO, TFLite, and TVM Hands-on with TensorFlow, PyTorch, scikit-learn, and signal/image processing techniques Proficient in designing for real-time inference on resource-constrained platforms Exposure to AI accelerators, NPUs, DSPs, and hybrid SoC environments; must have exposure to NVIDIA SoC & Tools Presales, account engagement, and solutioning experience with North American or European clients ________________________________________ Nice to Have: Cloud-edge integration using AWS Greengrass, Azure IoT Edge, GCP Edge TPU Understanding of AI regulatory/safety standards (ISO, IEC, FDA compliance for AI/ML in regulated industries) ________________________________________ Educational Criteria: BE/ME/B.Tech/M.Tech Electronics, Computer Science, AI/ML, Embedded Systems, or Data Science ________________________________________ Travel: Flexibility to travel globally with sales or delivery teams for customer meetings, workshops, and project deployments as needed. Interested and qualified candidate can directly reach Mr. Anup Sharma at 99099-75421 or anup.s@acldigital.com. (staffing partner can communicate over the email)

As a highly skilled Senior Machine Learning Engineer, you will leverage your expertise in Deep Learning, Large Language Models (LLMs), and MLOps/LLMOps to design, optimize, and deploy cutting-edge AI solutions. Your responsibilities will include developing and scaling deep learning models, fine-tuning LLMs (e.g., GPT, Llama), and implementing robust deployment pipelines for production environments. You will be responsible for designing, training, fine-tuning, and optimizing deep learning models (CNNs, RNNs, Transformers) for various applications such as NLP, computer vision, or multimodal tasks. Additionally, you will fine-tune and adapt LLMs for domain-specific tasks like text generation, summarization, and semantic similarity. Experimenting with RLHF (Reinforcement Learning from Human Feedback) and alignment techniques will also be part of your role. In the realm of Deployment & Scalability (MLOps/LLMOps), you will build and maintain end-to-end ML pipelines for training, evaluation, and deployment. Deploying LLMs and deep learning models in production environments using frameworks like FastAPI, vLLM, or TensorRT is crucial. You will optimize models for low-latency, high-throughput inference and implement CI/CD workflows for ML systems using tools like MLflow and Kubeflow. Monitoring & Optimization will involve setting up logging, monitoring, and alerting for model performance metrics such as drift, latency, and accuracy. Collaborating with DevOps teams to ensure scalability, security, and cost-efficiency of deployed models will also be part of your responsibilities. The ideal candidate will possess 5-7 years of hands-on experience in Deep Learning, NLP, and LLMs. Strong proficiency in Python, PyTorch, TensorFlow, Hugging Face Transformers, and LLM frameworks is essential. Experience with model deployment tools like Docker, Kubernetes, and FastAPI, along with knowledge of MLOps/LLMOps best practices and familiarity with cloud platforms (AWS, GCP, Azure) are required qualifications. Preferred qualifications include contributions to open-source LLM projects, showcasing your commitment to advancing the field of machine learning.,

You are seeking a hands-on backend expert to elevate your FastAPI-based platform to the next level by developing production-grade model-inference services, agentic AI workflows, and seamless integration with third-party LLMs and NLP tooling. In this role, you will be responsible for various key areas: 1. Core Backend Enhancements: - Building APIs - Strengthening security with OAuth2/JWT, rate-limiting, SecretManager, and enhancing observability through structured logging and tracing - Adding CI/CD, test automation, health checks, and SLO dashboards 2. Awesome UI Interfaces: - Developing UI interfaces using React.js/Next.js, Redact/Context, and various CSS frameworks like Tailwind, MUI, Custom-CSS, and Shadcn 3. LLM & Agentic Services: - Designing micro/mini-services to host and route to platforms such as OpenAI, Anthropic, local HF models, embeddings & RAG pipelines - Implementing autonomous/recursive agents that orchestrate multi-step chains for Tools, Memory, and Planning 4. Model-Inference Infrastructure: - Setting up GPU/CPU inference servers behind an API gateway - Optimizing throughput with techniques like batching, streaming, quantization, and caching using tools like Redis and pgvector 5. NLP & Data Services: - Managing the NLP stack with Transformers for classification, extraction, and embedding generation - Building data pipelines to combine aggregated business metrics with model telemetry for analytics You will be working with a tech stack that includes Python, FastAPI, Starlette, Pydantic, Async SQLAlchemy, Postgres, Docker, Kubernetes, AWS/GCP, Redis, RabbitMQ, Celery, Prometheus, Grafana, OpenTelemetry, and more. Experience in building production Python REST APIs, SQL schema design in Postgres, async patterns & concurrency, UI application development, RAG, LLM/embedding workflows, cloud container orchestration, and CI/CD pipelines is essential for this role. Additionally, experience with streaming protocols, NGINX Ingress, SaaS security hardening, data privacy, event-sourced data models, and other related technologies would be advantageous. This role offers the opportunity to work on evolving products, tackle real challenges, and lead the scaling of AI services while working closely with the founder to shape the future of the platform. If you are looking for meaningful ownership and the chance to solve forward-looking problems, this role could be the right fit for you.,

Our vision is to transform how the world uses information to enrich life for . 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.

Key Responsibilities: Design and optimize model serving infrastructure with a focus on low latency and cost efficiency Build scalable inference pipelines across different hardware acceleration options Implement monitoring and observability solutions for ML systems Collaborate with ML Engineers to define best practices for deployment Develop enterprise-grade, cost-efficient ML solutions Work closely with MLEs, QA, and DevOps teams in a distributed environment Evaluate new technologies and contribute to system architecture decisions Drive continuous improvements in ML infrastructure Required Experience & Skills: 5+ years of experience in software engineering using Python Hands-on experience with ML frameworks (especially PyTorch) Experience optimizing ML models using hardware accelerators (e.g., AWS Neuron, ONNX, TensorRT) Familiarity with AWS ML services and hardware-accelerated compute (e.g., SageMaker, Inferentia, Trainium) Proven ability to build and maintain serverless architectures on AWS Strong understanding of event-driven patterns (SQS/SNS) and caching strategies Proficiency with Docker and container orchestration tools Solid grasp of RESTful API design and implementation Focus on secure, high-quality code with experience using static code analysis tools Strong problem-solving, algorithmic thinking, and communication skills

Responsibilities: Develop and optimize computer vision models for tasks like object detection, image segmentation, and multi-object tracking. Lead research on novel techniques using deep learning frameworks (TensorFlow, PyTorch, JAX). Build efficient computer vision pipelines and optimize models for real-time performance. Deploy models using microservices (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure). Lead MLOps practices, including CI/CD pipelines, model versioning, and training optimizations. Required Skills: Expert in Python, OpenCV, NumPy, and deep learning architectures (e.g., ViTs, YOLO, Mask R-CNN). Strong knowledge in computer vision fundamentals, including feature extraction and multi-view geometry with experience in deploying and optimizing models with TensorRT, Open VINO, and cloud/edge solutions. Proficient with MLOps tools (MLflow, DVC), CI/CD, and distributed training frameworks. Experience in 3D vision, AR/VR, or LiDAR processing is a plus. Nice to Have: Experience with multi-camera vision systems, LiDAR, sensor fusion, and reinforcement learning for vision tasks. Exposure to generative AI models (e.g., Stable Diffusion, GANs) and large-scale image processing (Apache Spark, Dask). Research publications or patents in computer vision and deep learning.

General Summary: 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 Systems Engineer, you will research, design, develop, simulate, and/or validate systems-level software, hardware, architecture, algorithms, and solutions that enables the development of cutting-edge technology. Qualcomm Systems Engineers collaborate across functional teams to meet and exceed system-level requirements and standards. Minimum Qualifications: Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 8+ years of Systems Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 7+ years of Systems Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field and 6+ years of Systems Engineering or related work experience. Principal Engineer Machine Learning We are looking for a Principal AI/ML Engineer with expertise in model inference , optimization , debugging , and hardware acceleration . This role will focus on building efficient AI inference systems, debugging deep learning models, optimizing AI workloads for low latency, and accelerating deployment across diverse hardware platforms. In addition to hands-on engineering, this role involves cutting-edge research in efficient deep learning, model compression, quantization, and AI hardware-aware optimization techniques . You will explore and implement state-of-the-art AI acceleration methods while collaborating with researchers, industry experts, and open-source communities to push the boundaries of AI performance. This is an exciting opportunity for someone passionate about both applied AI development and AI research , with a strong focus on real-world deployment, model interpretability, and high-performance inference . Education & Experience: 20+ years of experience in AI/ML development, with at least 5 years in model inference, optimization, debugging, and Python-based AI deployment. Masters or Ph.D. in Computer Science, Machine Learning, AI Leadership & Collaboration Lead a team of AI engineers in Python-based AI inference development . Collaborate with ML researchers, software engineers, and DevOps teams to deploy optimized AI solutions. Define and enforce best practices for debugging and optimizing AI models Key Responsibilities Model Optimization & Quantization Optimize deep learning models using quantization (INT8, INT4, mixed precision etc), pruning, and knowledge distillation . Implement Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT) for deployment. Familiarity with TensorRT, ONNX Runtime, OpenVINO, TVM AI Hardware Acceleration & Deployment Optimize AI workloads for Qualcomm Hexagon DSP, GPUs (CUDA, Tensor Cores), TPUs, NPUs, FPGAs, Habana Gaudi, Apple Neural Engine . Leverage Python APIs for hardware-specific acceleration , including cuDNN, XLA, MLIR . Benchmark models on AI hardware architectures and debug performance issues AI Research & Innovation Conduct state-of-the-art research on AI inference efficiency, model compression, low-bit precision, sparse computing, and algorithmic acceleration . Explore new deep learning architectures (Sparse Transformers, Mixture of Experts, Flash Attention) for better inference performance . Contribute to open-source AI projects and publish findings in top-tier ML conferences (NeurIPS, ICML, CVPR). Collaborate with hardware vendors and AI research teams to optimize deep learning models for next-gen AI accelerators. Details of Expertise: Experience optimizing LLMs, LVMs, LMMs for inference Experience with deep learning frameworks : TensorFlow, PyTorch, JAX, ONNX. Advanced skills in model quantization, pruning, and compression . Proficiency in CUDA programming and Python GPU acceleration using cuPy, Numba, and TensorRT . Hands-on experience with ML inference runtimes (TensorRT, TVM, ONNX Runtime, OpenVINO) Experience working with RunTimes Delegates (TFLite, ONNX, Qualcomm) Strong expertise in Python programming , writing optimized and scalable AI code. Experience with debugging AI models , including examining computation graphs using Netron Viewer, TensorBoard, and ONNX Runtime Debugger . Strong debugging skills using profiling tools (PyTorch Profiler, TensorFlow Profiler, cProfile, Nsight Systems, perf, Py-Spy) . Expertise in cloud-based AI inference (AWS Inferentia, Azure ML, GCP AI Platform, Habana Gaudi). Knowledge of hardware-aware optimizations (oneDNN, XLA, cuDNN, ROCm, MLIR, SparseML). Contributions to open-source community Publications in International forums conferences journals

We are seeking a highly skilled Generative AI Consulting Director to join our dynamic team, where they will lead our consulting team, manage the delivery of consulting services, guide clients through the implementation of our Gen AI platform, and ensure the successful adoption of the platform across industries. Key Responsibilities: Lead or mentor a global team of AI consultants, solution architects, and professional services teams. Develop and execute the strategy for consulting and professional services for the Gen AI platform. Manage the end-to-end Implementation our platform in client environments, ensuring high quality implementations, on time delivery, and alignment with customer expectations. Work closely with clients to understand their business challenges and design tailored solutions using the Gen AI platform. Lead the development of solution architectures, ensuring that proposed solutions are scalable, innovative, and aligned with the client's objectives. Collaborate with product development, GTM, and engineering teams to ensure successful implementation and integrations. Provide feedback to product teams based on client needs and market trends to continuously improve the platforms offerings. Drive client success by ensuring that Gen AI platform implementation deliver measurable value and return on investment (ROI). Work closely with clients to define successful metrics, track project outcomes, and guide the optimization of AI models and systems post-implementation. Manage P&L for the Consulting and Professional Services division, ensuring profitability through effective project management, cost control, and client retention. Develop and implement strategies to drive revenue growth within the professional services arm. Ethical and Responsible AI: Adhere to ethical AI practices, such as fairness, transparency, and accountability. Address biases and potential risks associated with AI systems to ensure responsible deployment and usage. Research and Innovation: Stay updated with the latest advancements in AI technologies, frameworks, and algorithms. Conduct research and experimentation to explore innovative approaches and techniques that can enhance AI capabilities. Mandatory Qualifications/Skills: A bachelors or masters degree, or equivalent, in computer science, Artificial Intelligence, or a related field. 15+ years of experience in consulting or professional services, with at least 5 years in a leadership role overseeing a team of AI consultants or solution architects Extensive experience in delivering Generative AI solutions and familiarity with AI platforms, including knowledge of NLP, deep learning, and reinforcement learning. Experience with large language models (LLMs) and prompt engineering. Solid understanding of various fine-tuning techniques like full fine tuning, PEFT techniques like LoRA, QLoRA and the strategy to adopt for various use cases Proficiency in languages such as Python, Scala, or Java In depth knowledge of both relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., Vector databases, MongoDB, Cassandra etc). Expertise in Gen AI/AI libraries / frameworks, including but not limited to LangChain, LangGraph, LangSmith, TensorFlow, PyTorch, scikit and Keras Proven understanding of cloud computing platforms (e.g., AWS, Azure, Google Cloud) and experience deploying AI models on these platforms. Proven experience in managing client relationships and understanding their business needs to deliver successful AI solutions. Strong understanding of AI systems architecture and the ability to design and implement complex AI solutions for clients across various industries. Experience with project management methodologies, and a proven ability to manage large, complex projects to successful completion. Excellent leadership, mentoring, and team building skills, with a track record of developing high performing teams. Strong business acumen, with the ability to balance technical expertise with client centric decision making. Outstanding communication and presentation skills, capable of engaging with senior executives and non-technical stakeholders. Strong problem solving and analytical skills, with the ability to think creatively and provide innovative solutions Preferred Skills: Knowledge of NVIDIA CUDA, cuDNN, TensorRT, and experience with NVIDIA GPU hardware and the software stack. Familiarity with High Performance Computing (HPC) and their integration of AI workloads. Familiarity with Big Data platforms and technologies, such as Hadoop or Apache Spark and their integration with AI solutions.

Job Overview We are looking for a Senior Computer Vision Machine Learning Engineer to lead the development of real-time CV/ML systems, with an emphasis on deploying models on edge platforms like the NVIDIA IGX Orin. The ideal candidate will have experience in designing robust vision pipelines, training and optimizing deep learning models, and working closely with hardware platforms for deployment. Responsibilities Lead the design, development, and deployment of end-to-end computer vision and deep learning models Optimize and deploy CV/ML pipelines on edge platforms, particularly NVIDIA IGX (Orin preferred) Work with cross-functional teams to integrate models into real-time applications (e.g., robotics, safety systems, industrial inspection) Develop and maintain datasets, perform data augmentation, and ensure quality training inputs Leverage NVIDIA SDKs (e.g., DeepStream, TensorRT, TAO Toolkit, CUDA) for performance and acceleration Collaborate with hardware engineers to fine-tune models for power, latency, and throughput constraints Stay up to date with the latest research and techniques in computer vision, edge AI, and embedded ML Requirements Bachelors or Masters degree in Computer Science, Electrical Engineering, or related field 5+ years of experience in Computer Vision and Machine Learning (deep learning emphasis) Proficiency in Python, C++, TensorFlow, PyTorch Strong understanding of model optimization techniques for edge deployment Hands-on experience with NVIDIA platforms IGX, Jetson, or Xavier (IGX Orin highly preferred) Experience with NVIDIA SDKs (e.g., DeepStream, TensorRT, CUDA, TAO Toolkit) Solid knowledge of vision tasks: object detection, tracking, classification, segmentation Familiarity with containerization (Docker), CI/CD pipelines, and version control (Git) Preferred Qualifications Experience in industrial AI, medical imaging, or robotics Exposure to RTOS, safety-critical systems, or IEC 61508/ISO 26262 environments Familiarity with ONNX, OpenCV, ROS, or GStreamer What We Offer Opportunity to work on cutting-edge AI/edge technology with real-world impact Collaborative and fast-paced engineering culture Flexible working hours and remote work options Competitive salary and benefits package Location-Remote,Delhi NCR,Bangalore,Chennai,Pune,Kolkata,Ahmedabad,Mumbai,Hyderabad

About The Role Were building an agentic AI platform that turns one line of text and a video feed into end-to-end, real-time computer-vision solutionsthink semantic video search, object / action recognition, and task-oriented visual agents deployable with a single click As a Gen AI ML Engineer, youll architect the core vision & multimodal-reasoning stack and pave the road from prototype to production. Roles And Responsibilities Semantic video search Ship a pipeline that allows users to type show every forklift near aisle 5 in the last 30 minutes and get keyed-off clips in Wire embeddings to a hybrid FAISS/HNSW index; surface results through a simple REST & React playground. Create agentic pipelines Chain vision language models and zero/few-shot vision models with LLM planners (Gemini, GPT-4o, AutoGen, etc.) so a single prompt becomes a multi-step perception workflow. Profile and accelerate inference (TensorRT, ONNX, quantization, batching) to meet latency / throughput targets on GPU and CPU fleets. Rapid prototyping loops Run weekly paper-to-prototype spikes: reproduce a fresh arXiv idea, benchmark, and decide go/no-go in Hand successful python scripts & checkpoints to MLOps for productionizationno plumbing marathons. Data & Evaluation Spin up scalable pipelines for video ingestion, labeling (active learning, weak supervision), experiment tracking, and continuous evaluation. Collaborate & Lead Partner with product and ML Ops engineers; set research direction, mentor future hires, and establish best practices. Must-have Skill Set 13 years deep-learning research experience (internships & grad work count). Fluency in Python + PyTorch; comfortable hacking large vision/LLM repos. Proof you ship ideasfirst-author paper, OSS repo, Kaggle medal, or faithful reproduction of a cutting-edge model. Hands-on with LLM prompting/fine-tuning and at least one agent framework. Able to turn fuzzy product asks into measurable experiments and explain results clearly. Bonus Cred Large-scale video retrieval or temporal grounding experience. Prior work building agentic-AI pipelines that combine perception models with LLM reasoning. Open-source contributions to GenAI/vision libs (OpenCLIP, Vid2Seq, ViperGPT, etc.). What can you expect? Ability to shape the future of manufacturing by leveraging best-in-class AI and software; we are a unique organization with niche skill set that you would also develop while working with us World class work culture, coaching and development Mentoring from highly experienced leadership from world class companies (refer to Ripik.AI website for details) International exposure Work Location NOIDA (Work from Office)

Senior Software Technical Director / Software Technical Director Bangalore Founded in 2023,by Industry veterans HQ in California,US We are revolutionizing sustainable AI compute through intuitive software with composable silicon We are looking for a Software Technical Director with a strong technical foundation in systems software, Linux platforms, or machine learning compiler stacks to lead and grow a high-impact engineering team in Bangalore. You will be responsible for shaping the architecture, contributing to codebases, and managing execution across projects that sit at the intersection of systems programming, AI runtimes, and performance-critical software. Key Responsibilities: Technical Leadership: Lead the design and development of Linux platform software, firmware, or ML compilers and runtimes. Drive architecture decisions across compiler, runtime, or low-level platform components. Write production-grade C++ code and perform detailed code reviews. Guide performance analysis and debugging across the full stackfrom firmware and drivers to user-level runtime libraries. Collaborate with architects, silicon teams, and ML researchers to build future-proof software stacks. Team & Project Management: Mentor and coach junior and senior engineers to grow technical depth and autonomy. Own end-to-end project planning, execution, and delivery, ensuring high-quality output across sprints/releases. Facilitate strong cross-functional communication with hardware, product, and other software teams globally. Recruit and grow a top-tier engineering team in Bangalore, contributing to the hiring strategy and team culture. Required Qualifications: Bachelors or Master’s degree in Computer Science, Electrical Engineering, or related field. 18+ years of experience in systems software development with significant time spent in C++, including architectural and hands-on roles. Proven experience in either: Linux kernel, bootloaders, firmware, or low-level platform software, or Machine Learning compilers (e.g., MLIR, TVM, Glow) or runtimes (e.g., ONNX Runtime, TensorRT, vLLM). Excellent communication skills—written and verbal. Prior experience in project leadership or engineering management with direct reports. Highly Desirable: Understanding of AI/ML compute workloads, particularly Large Language Models (LLMs). Familiarity with performance profiling, bottleneck analysis, and compiler-level optimizations. Exposure to AI accelerators, systolic arrays, or vector SIMD programming. Why Join Us? Work at the forefront of AI systems software, shaping the future of ML compilers and runtimes. Collaborate with globally distributed teams in a fast-paced, innovation-driven environment. Build and lead a technically elite team from the ground up in a growth-stage organization. Contact: Uday Mulya Technologies muday_bhaskar@yahoo.com "Mining The Knowledge Community"