57 Tensorrt Jobs

The opportunity We are currently looking for a Computer Vision Engineer, to join our office in Pune. As a Computer Vision Engineer, you will support our clients in defining and implementing a data journey aligned with their strategic objectives. Some of your responsibilities will include: Work with the research team to research, develop, evaluate, and optimize various computer vision and deep learning models for different problems. Take ownership to drive computer vision solutions and meet customer requirements. Deploying developed computer vision models on edge devices after optimization to meet customer requirements and maintain them to later improve to address additional customer requirements in the future. Developing data handling and machine learning pipelines for training In-depth understanding of computer vision models including object detection, semantic segmentation, and key-point detection Implementing algorithms in robust, efficient, and well-tested code. Skills and attributes for success To qualify for the role, you must have Experience of minimum 2 years Ability to develop Deep Learning frameworks to solve problems. Design and create platforms for image processing and visualization. Knowledge of computer vision libraries. Understanding of dataflow programming. B.E. (E&TC /Computer / IT / Mechanical / Electronics) C++, video analytics, CUDA, Deepstream

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) Show more Show less

As a skilled engineer at SiteRecon, a B2B SaaS platform revolutionizing property measurements for landscapers and snow removal contractors in the $176B US market, you will play a crucial role in evaluating, enhancing, and transforming the computer vision infrastructure. Your expertise will be instrumental in transitioning from traditional CNN architectures to cutting-edge transformer-based models. You will be responsible for model design, training, and optimization to meet the demands of this frontier challenge in computer vision and GIS. You will have access to a vast aerial imagery database with a ground sample distance of 7 cm, covering 500,000+ properties across the U.S. since 2021. Additionally, you will collaborate with a team of 60 annotators mapping thousands of acres daily and have a ready market of 350+ customers for immediate deployment. Leveraging powerful compute resources for rapid model training, you will be at the forefront of solving a constrained problem using the world's highest-resolution aerial imagery to develop scalable templates for automated extraction in GIS applications. Your key responsibilities will include designing and implementing transformer-based architecture for semantic segmentation of aerial imagery, developing efficient image-to-token and token-to-image conversion pipelines, creating and maintaining training datasets, optimizing model training processes for distributed computing environments, implementing efficient inference pipelines for production deployment, and collaborating with the engineering team to integrate new models into the existing infrastructure. To excel in this role, you are expected to have a strong foundation in computer vision and deep learning fundamentals, extensive experience in training transformer models from scratch, expert-level proficiency in PyTorch, experience with ONNX or TensorRT model optimization and deployment, deep understanding of distributed computing and parallel processing, advanced Python knowledge, experience with semantic segmentation tasks, and a proven track record of handling large-scale data processing. This position requires a minimum of 5 years of hands-on deep learning experience, a track record of successfully deploying computer vision models in production, experience with vision transformer architectures, experience optimizing models for production using ONNX/TensorRT, and a background in handling high-resolution satellite/aerial imagery. A Masters/PhD in Computer Science, Machine Learning, or a related field is preferred. Your desired qualities should include a strong mathematical foundation in deep learning concepts, experience with model architecture design and optimization, the ability to conduct independent research and stay updated with the latest developments, excellence in technical documentation and communication, and self-motivation to solve complex technical challenges efficiently. What sets you apart in this role is your experience with vision transformers specifically for segmentation tasks, published research or contributions to open-source computer vision projects, experience with high-performance computing environments, a background in geospatial data processing, hands-on experience with model quantization and optimization using ONNX/TensorRT, and experience deploying optimized models in production environments. Your role is crucial as every day without improved segmentation costs real business opportunities, and we need someone who can think systematically, move fast, and deliver production-ready improvements promptly.,

About BeGig BeGig is the leading tech freelancing marketplace. We empower innovative, early-stage, non-tech founders to bring their visions to life by connecting them with top-tier freelance talent. By joining BeGig, youre not just taking on one roleyoure signing up for a platform that will continuously match you with high-impact opportunities tailored to your expertise. Your Opportunit yJoin our network as an Edge AI Develope r and bring AI capabilities directly to edge deviceswhere speed, privacy, and offline access matter most. Youll develop, deploy, and optimize machine learning models to run on low-power, low-latency hardware in real-world environments like IoT, robotics, automotive, and smart devices . This fully remote role is available on an hourly or project-based basi s.Role Overvi ewAs an Edge AI Developer, you wil l:Build AI for the Ed ge: Develop and deploy optimized AI models that run directly on embedded or edge device s.Model Optimizati on: Use techniques like quantization, pruning, and compression to reduce model size and inference latenc y.Hardware Integrati on: Work with edge hardware platforms such as NVIDIA Jetson, Raspberry Pi, Coral TPU, and ARM-based board s.Deploy Offline Mode ls: Package and deploy models for inference without requiring constant cloud connectivit y.Performance Tuni ng: Ensure models are fast, accurate, and resource-efficient under real-time constraint s.Toolchain Usa ge: Use platforms like TensorFlow Lite, ONNX, OpenVINO, or PyTorch Mobile for deployment and optimizatio n. Technical Requirements & Ski llsExperie nce: Minimum 2+ years in machine learning, embedded systems, or AI application developme nt.Model Optimizat ion: Experience with tools like TensorRT, TFLite, or ONNX Runtime for edge model optimizati on.Programm ing: Proficiency in Python and C/C++ for model integration, device communication, and real-time processi ng.Hardware Platfo rms: Familiarity with deploying AI models on Jetson Nano, Raspberry Pi, Intel Neural Compute Stick, e tc.Deployment & Test ing: Ability to build testing frameworks to simulate edge scenarios and monitor performan ce.Real-Time Considerati ons: Understanding of latency, thermal constraints, power management, and memory limitatio ns. What Were Looking ForA developer passionate about running AI outside the cloudon devices where speed, efficiency, and privacy are criti cal.A freelancer who can navigate hardware constraints and deliver smart, optimized ML models in product ion.A systems thinker who bridges the gap between machine learning research and embedded engineer ing. Why Joi n UsImmediate I mpact: Help startups deploy AI models into real-world environmentsfrom warehouses to smart h omes.Remote & Fle xible: Work from anywhere and structure your engagement on your own termshourly or project-b ased.Future Opportun ities: Be continuously matched with projects in IoT, robotics, and real-time edg e AI.Growth & Recogn ition: Be part of a trusted network that values cutting-edge technical expertise and applied AI deli very. Show more Show less

JOB TITLE: AI ASSOCIATE OPTIMIZATION Location: Bengaluru | Experience: 1-3 Years ABOUT NEWCOLD NewCold is a service provider in cold chain logistics with a focus on development and operation of large, highly automated cold stores. NewCold strives to be crucial in the cold chain of leading food companies, by offering advanced logistic services worldwide. NewCold is one of the fastest growing companies (over 2,000 employees) in the cold chain logistics and they are expanding teams to support this growth. They use the latest technology that empowers people, to handle food responsibly and guarantee food safety in a sustainable way. They challenge the industry, believe in long-term partnerships, and deliver solid investment opportunities that enable next generation logistic solutions. NewCold has leading market in-house expertise in designing, engineering, developing and operating state-of-the-art automated cold stores: a result of successful development and operation of over 15 automated warehouses across three continents. With the prospect of many new construction projects around the world in the very near future, this vacancy offers an interesting opportunity to join an internationally growing and ambitious organization. POSITION SUMMARY NewCold is seeking an AI Associate Optimization to enhance the performance and efficiency of our AI-powered solutions within our highly automated cold chain logistics network. This role focuses on optimizing machine learning models deployed in warehouse operations, ensuring low latency, high throughput, and accurate predictions for improved decision-making. You will be instrumental in bridging the gap between data science and real-world deployment, contributing to the continuous improvement of our automated systems. This position requires a strong understanding of model serving, containerization, and edge AI technologies. YOUR ROLE As an AI Associate Optimization, you will play a critical role in ensuring the reliability, scalability, and performance of AI models powering NewColds automated warehouse processes. You will be responsible for optimizing models for deployment across diverse infrastructure, including cloud and edge environments, directly impacting operational efficiency, cost reduction, and the overall effectiveness of our logistics solutions. Your work will contribute to maintaining NewColds competitive edge through cutting-edge AI implementation. KEY RESPONSIBILITIES Implement model optimization techniques such as quantization and knowledge distillation to reduce model size and improve inference speed for deployment on edge devices and cloud infrastructure. Develop and maintain CI/CD pipelines for automated model deployment and updates, ensuring seamless integration with existing systems. Benchmark and profile model performance (latency, throughput, memory usage) to identify bottlenecks and areas for improvement. Deploy and manage machine learning models using model serving frameworks like TensorFlow Serving, TorchServe, ONNX Runtime, or Triton Inference Server. Containerize AI models and applications using Docker and Podman for consistent and reproducible deployments. Collaborate with data scientists and software engineers to troubleshoot model performance issues and implement solutions. Monitor model performance in production and proactively address any degradation in accuracy or efficiency. Develop and maintain APIs/SDKs (REST, gRPC, FastAPI) for accessing and integrating AI models into various applications. Work with edge devices (NVIDIA Jetson, Coral TPU, ARM-based boards) and edge frameworks (TensorRT, OpenVINO, TFLite, TVM) to optimize models for low-power, real-time inference. WHAT WE ARE LOOKING FOR Bachelors or masters degree in computer science, Artificial Intelligence, Machine Learning or a related field 1-3 years of experience in a role focused on machine learning model optimization and deployment. Proficiency in Python and C++ programming languages. Hands-on experience with model serving frameworks (TensorFlow Serving, TorchServe, ONNX Runtime, Triton Inference Server). Experience with containerization technologies (Docker, Podman) and orchestration tools (Kubernetes, K3s, Edge orchestrators). Knowledge of model optimization techniques such as quantization and knowledge distillation. Familiarity with benchmarking and profiling tools for evaluating model performance. Strong analytical and problem-solving skills with a data-driven approach. Experience with CI/CD pipelines for ML deployment is highly desirable. Knowledge of edge devices (NVIDIAJetson, Coral TPU, ARM-based boards) and edge AI frameworks (TensorRT, OpenVINO, TFLite, TVM) is a significant plus. WHY JOIN US Opportunity to work on cutting-edge AI applications in a rapidly growing and innovative cold chain logistics company. Exposure to a wide range of AI technologies and challenges within a highly automated warehouse environment. Career growth potential within a dynamic and international organization. Collaborative and supportive team environment with opportunities for learning and development. Contribute to the development of next-generation logistics solutions that are shaping the future of the food supply chain. Show more Show less

We are seeking a highly experienced and skilled Machine Learning Software Engineer with 8-10 years of experience to join our team. The ideal candidate will be a deep learning expert with a strong background in optimizing and deploying machine learning models on specialized hardware, particularly ML accelerators. This role is critical for bridging the gap between theoretical model development and practical, high-performance inference on target platforms. A key focus of this position will be on model quantization and other optimization techniques to maximize efficiency and performance. Key Responsibilities : - Model Porting & Deployment : Port and deploy complex deep learning models from various frameworks (e.g., PyTorch, TensorFlow) to proprietary or commercial ML accelerator hardware platforms (e.g., TPUs, NPUs, GPUs). - Performance Optimization : Analyze and optimize the performance of ML models for target hardware, focusing on latency, throughput, and power consumption. - Quantization : Lead the efforts in model quantization (e.g., INT8, FP16) to reduce model size and accelerate inference while preserving model accuracy. - Profiling & Debugging : Utilize profiling tools to identify performance bottlenecks and debug issues in the ML inference pipeline on the accelerator - Collaboration : Work closely with the ML research, hardware, & software teams to understand model requirements and hardware capabilities, providing feedback to improve both. - Tooling & Automation : Develop and maintain tools and scripts to automate the model porting, quantization, and performance testing workflows - Research & Innovation : Stay current with the latest trends and research in ML hardware, model compression, and optimization techniques. Experience : - 8-10 years of professional experience in machine learning engineering, with a focus on model deployment and optimization. Technical Skills : - Deep expertise in deep learning frameworks such as PyTorch and TensorFlow. - Proven experience in optimizing models for inference on GPUs, NPUs, TPUs, or other specialized accelerators - Extensive hands-on experience with model quantization (e.g., Post-Training Quantization, Quantization-Aware Training). - Strong proficiency in C++ and Python, with experience writing highperformance, low-level code - Experience with GPU programming models like CUDA/cuDNN - Familiarity with ML inference engines and runtimes (e.g., TensorRT, OpenVINO, TensorFlow Lite). - Strong understanding of computer architecture principles, including memory hierarchies, SIMD/vectorization, and cache optimization - Version Control : Proficient with Git and collaborative development workflows - Education : Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field. Preferred Qualifications : - Experience with hardware-aware model design and co-design. - Knowledge of compiler technologies for deep learning. - Contributions to open-source ML optimization projects. - Experience with real-time or embedded systems. - Knowledge of cloud platforms (AWS, GCP, Azure) and MLOps best practices. - Familiarity with CI/CD pipelines and automated testing for ML models - Domain knowledge in areas like computer vision, natural language processing, or speech recognition.

As an AI Engineer at Smartan FitTech, you will play a pivotal role in spearheading the design, development, and deployment of cutting-edge computer vision models aimed at enhancing human motion understanding and video analytics. You will be at the forefront of the AI development lifecycle, contributing to the implementation of features that power our innovative fitness intelligence systems. This role requires a strong engineering focus on real-world deployment, performance optimization, and scalability, rather than academic prototyping. Your responsibilities will include overseeing the complete lifecycle of computer vision models, from initial experimentation to deployment and ongoing monitoring. You will be tasked with constructing and refining deep learning models for various applications such as pose estimation, activity recognition, and posture correction. Leveraging techniques like transfer learning, fine-tuning, and knowledge distillation will be essential for achieving optimal performance and generalization. Additionally, you will be responsible for designing scalable data pipelines for video ingestion, annotation, preprocessing, and augmentation, as well as integrating AI modules into cloud-based environments using REST APIs or microservices. To excel in this role, you should possess a Bachelor's or Master's degree in computer science, AI, machine learning, or a related field, along with at least 2 years of experience deploying computer vision models in production environments. Proficiency in Python, particularly with PyTorch and/or TensorFlow, is crucial. Hands-on expertise with models like YOLOv5/YOLOv8, Vision Transformers, ResNet, and others, as well as experience with pose estimation frameworks and real-time motion tracking, will be highly beneficial. Familiarity with tools like OpenCV, MMAction2, DeepSort, and ONNX/TensorRT is also desired. In this role, you will have the opportunity to shape the future of intelligent fitness systems, with autonomy and ownership over the deployment of models that have a real-world impact. You will thrive in a fast-paced environment that offers exposure to the entire AI pipeline, from data processing to deployment, within a collaborative team culture that values engineering excellence and innovation.,

You are an exceptional AI Architect with expertise in Agentic AI and Generative AI, responsible for leading the design, development, and deployment of next-generation autonomous AI systems. Your role involves building LLM-powered agents with memory, tool use, planning, and reasoning capabilities to create intelligent, goal-driven systems. As a technical leader, you will oversee end-to-end AI initiatives, from research and architecture design to deployment in cloud environments. Your key responsibilities include designing and implementing LLM-based agents for autonomous task execution, memory management, tool usage, and multi-step reasoning. You will develop modular, goal-oriented agentic systems using tools like LangChain, Auto-GPT, CrewAI, SuperAGI, and OpenAI Function Calling. Additionally, you will design multi-agent ecosystems with collaboration, negotiation, and task delegation capabilities while integrating long-term and short-term memory into agents. You will also be responsible for developing, fine-tuning, and optimizing foundation models like LLMs and diffusion models using TensorFlow, PyTorch, or JAX. Applying model compression, quantization, pruning, and distillation techniques for deployment efficiency will be part of your role. Leveraging cloud AI services such as AWS SageMaker, Azure ML, and Google Vertex AI for scalable model training and serving is also crucial. Your tasks will include leading research in Agentic AI, LLM orchestration, and advanced planning strategies. Staying updated with state-of-the-art research and contributing to whitepapers, blogs, or conferences like NeurIPS, ICML, and ICLR will be expected. Evaluating new architectures such as BDI models, cognitive architectures, or neuro-symbolic approaches will be part of your responsibilities. Strong coding proficiency in Python, CUDA, and TensorRT for model acceleration is required, along with experience in distributed computing frameworks like Ray, Dask, and Apache Spark for training large-scale models. Designing and implementing robust MLOps pipelines using Docker, Kubernetes, MLflow, and CI/CD systems is essential. To excel in this role, you should have at least 8-14 years of experience in AI/ML, with a minimum of 2+ years of hands-on experience with Agentic AI systems. Proven experience in building, scaling, and deploying agent-based architectures is necessary. A strong theoretical foundation in machine learning, deep learning, NLP, and reinforcement learning is crucial, along with familiarity in cognitive architectures, decision-making, and planning systems. Hands-on experience with LLM integration and fine-tuning, including OpenAI GPT-4, Claude, LLaMA, Mistral, and Gemini, is required. Preferred qualifications include publications or open-source contributions in Agentic AI or Generative AI, experience with simulation environments like OpenAI Gym or Unity ML-Agents for training/test agents, and knowledge of safety, ethics, and alignment in autonomous AI systems.,

The ideal candidate for the AI/ML position in Bangalore should have a minimum of 6 to 12 years of experience and possess the following must-have skills: - A thorough understanding of AI/ML concepts, Deep Learning, Computer Vision, Natural Language Processing, and Generative AI. - Prior experience in working with DL models, specifically in Computer Vision with frameworks like ResNet, YOLO v3, v4, v5, Efficient Det, etc. - Proficiency in developing Computer Vision applications using DL Frameworks such as TensorFlow, Caffe, Torch, and Toolkits like OpenVINO and TensorRT in both C++ and Python. - Conducting Functional, Performance Tests, and Accuracy Tests on DL Models. - Familiarity with Open Source libraries related to Computer Vision, DL Frameworks, and Toolkits including OpenVINO and TensorRT. - Strong programming skills in C++, with knowledge of the latest standards (C++17) and a background in Object-Oriented Programming. - Experience in performance optimization, developing mathematical routines/kernels with strict performance constraints. Additionally, the following skills are considered good to have: - Exposure to Version Control, Software integration, Continuous Integration, DevOps, Build Tools such as cmake, gcc toolchain, MSVC, etc. - Demonstrated ability to troubleshoot and debug problems in complex systems involving multiple proprietary and open-source components. - Experience in programming on Nvidia GPUs and a solid understanding of the usage of cuDNN and CUDA Libraries. If you possess the required expertise and are enthusiastic about working with cutting-edge technology, we encourage you to apply for this position or reach out to us for more information.,

Principal Engineer (Medtech) About the Role We are seeking a highly experienced Principal Engineer to join our MedTech engineering team.The ideal candidate will bring deep expertise in Cloud, Video technologies, and Edge AI to design and deliver innovative healthcare solutions. This role requires strong technical leadership, hands-on engineering skills, and a proven background in the MedTech domain. Key Responsibilities Lead architecture, design, and implementation of MedTech solutions using Cloud, Video, and Edge AI technologies. Partner with cross-functional teams (Product, Research, and Engineering) to deliver scalable, secure, and compliant healthcare applications. Drive innovation in connected health, video-based diagnostics, and AI at the edge. Ensure adherence to healthcare compliance standards (HIPAA, FDA, MDR, etc.). Mentor and guide engineering teams, fostering technical excellence and best practices. Evaluate new tools, technologies, and frameworks to enhance product capabilities. Required Skills & Experience 12+ years of experience in software engineering with strong exposure to MedTech / Healthcare domain. Proven expertise in Cloud platforms (AWS, Azure, or GCP). Strong knowledge of Video technologies (video streaming, processing, WebRTC, RTP/RTSP). Hands-on experience with Edge AI frameworks (TensorRT, OpenVINO, or similar). Proficiency in microservices, APIs, containerization (Docker, Kubernetes). Strong focus on data security, privacy, and compliance in healthcare applications. Excellent leadership, communication, and problem-solving skills. Preferred Qualifications Experience in IoMT (Internet of Medical Things) or connected devices. Background in medical imaging, diagnostics, or remote patient monitoring. Contributions to patents, publications, or open-source initiatives. Location-Remote, Delhi NCR, Bangalore, Chennai, Pune, Kolkata, Ahmedabad, Mumbai, Hyderabad

Role & responsibilities Job Title / Designation : Practice Lead AI/ML Business Unit : Embedded Engineering Services (EES) Industry Experience Range : 8+ years Job Location : Preferably Pune or Bangalore, Ahmedabad 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: 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 Please share resume at anup.s@acldigital.com, candidate can directly call on cell number 99099-75421 for more details.

Dharmakit Networks is a premium global IT solutions partner dedicated to innovation and success worldwide. Specializing in website development, SaaS, digital marketing, AI Solutions, and more, we help brands turn their ideas into high-impact digital products. Known for blending global standards with deep Indian insight, we are now stepping into our most exciting chapter yet. Project Ax1 is our next-generation Large Language Model (LLM), a powerful AI initiative designed to make intelligence accessible and impactful for Bharat and the world. Built by a team of AI experts, Dharmakit Networks is committed to developing cost-effective, high-performance AI tailored for India and beyond, enabling enterprises to unlock new opportunities and drive deeper connections. Join us in reshaping the future of AI, starting from India. As a GPU Infrastructure Engineer, you will be at the core of building, optimizing, and scaling the GPU and AI compute infrastructure that powers Project Ax1. Your responsibilities will include designing, deploying, and optimizing GPU infrastructure for large-scale AI workloads, managing GPU clusters across cloud (AWS, Azure, GCP) and on-prem setups, setting up and maintaining model CI/CD pipelines for efficient training and deployment, optimizing LLM inference using TensorRT, ONNX, Nvidia NVCF, and more. You will also be responsible for managing offline/edge deployments of AI models, building and tuning data pipelines to support real-time and batch processing, monitoring model and infra performance for availability, latency, and cost efficiency, and implementing logging, monitoring, and alerting using tools like Prometheus, Grafana, ELK, CloudWatch. Collaboration with AI Experts, ML Experts, backend Experts, and full-stack teams will be essential to ensure seamless model delivery. **Key Responsibilities:** - Design, deploy, and optimize GPU infrastructure for large-scale AI workloads. - Manage GPU clusters across cloud (AWS, Azure, GCP) and on-prem setups. - Set up and maintain model CI/CD pipelines for efficient training and deployment. - Optimize LLM inference using TensorRT, ONNX, Nvidia NVCF, etc. - Manage offline/edge deployments of AI models (e.g., CUDA, Lambda, containerized AI). - Build and tune data pipelines to support real-time and batch processing. - Monitor model and infra performance for availability, latency, and cost efficiency. - Implement logging, monitoring, and alerting using Prometheus, Grafana, ELK, CloudWatch. - Work closely with AI Experts, ML Experts, backend Experts, and full-stack teams to ensure seamless model delivery. **Must-Have Skills And Qualifications:** - Bachelors degree in Computer Science, Engineering, or related field. - Hands-on experience with Nvidia GPUs, CUDA, and deep learning model deployment. - Strong experience with AWS, Azure, or GCP GPU instance setup and scaling. - Proficiency in model CI/CD and automated ML workflows. - Experience with Terraform, Kubernetes, and Docker. - Familiarity with offline/edge AI, including quantization and optimization. - Logging & Monitoring using tools like Prometheus, Grafana, CloudWatch. - Experience with backend APIs, data processing workflows, and ML pipelines. - Experience with Git, collaboration in agile, cross-functional teams. - Strong analytical and debugging skills. - Excellent communication, teamwork, and problem-solving abilities. **Good To Have:** - Experience with Nvidia NVCF, DeepSpeed, vLLM, Hugging Face Triton. - Knowledge of FP16/INT8 quantization, pruning, and other optimization tricks. - Exposure to serverless AI inference (Lambda, SageMaker, Azure ML). - Contributions to open-source AI infrastructure projects or a strong GitHub portfolio showcasing ML model deployment expertise.,

As a Staff Software Development Engineer in System Optimization at Netradyne, you will be responsible for implementing and evolving processes to ensure the efficient and high-performance operation of our in-house designed driver monitoring and assistance technology. Your key responsibilities will include crafting and developing tools, frameworks, and reporting mechanisms for system optimization, streamlining software deployment across IoT devices, enhancing data platforms, and optimizing machine learning models deployed on the platform. Additionally, you will be improving driver monitoring and assistance algorithms to boost system efficiency, managing production inquiries, and ensuring overall application stability in production environments. Your role will also involve effectively conveying highly technical results to diverse audiences. To excel in this role, you should hold a B.E/B.Tech or M.E/M.Tech degree with a minimum of 6+ years of experience in software system optimization. You should possess exceptional attention to detail, strong analytical skills, and a creative mindset dedicated to achieving optimal system performance. Proficiency in programming languages such as C/C++, OpenGL, CUDA, and Python is required, along with a solid grasp of basic statistics, probability, and concepts in machine learning (ML) and computer vision (CV). Experience with ML frameworks like Caffe, TensorRT, OpenCL, SNPE, OpenVino, and ONNX, as well as expertise in embedded platforms, make files, build systems, and familiarity with Jenkins, will be valuable assets in this role. Netradyne is actively seeking talented engineers to join our Analytics team, particularly individuals with a strong educational background and past experience in IoT-related companies. If you have prior experience in IoT-related fields and are passionate about optimizing systems and software development, we encourage you to apply and be a part of our innovative team. Join us as a Staff Software Development Engineer in System Optimization and play a pivotal role in enhancing the efficiency and performance of our cutting-edge technology.,

Silicon Labs (NASDAQ: SLAB) is the leading innovator in low-power wireless connectivity, building embedded technology that connects devices and improves lives. Merging cutting-edge technology into the worlds most highly integrated SoCs, Silicon Labs provides device makers the solutions, support, and ecosystems needed to create advanced edge connectivity applications. Headquartered in Austin, Texas, Silicon Labs has operations in over 16 countries and is the trusted partner for innovative solutions in the smart home, industrial IoT, and smart cities markets. Learn more at www.silabs.com. The Role As a Senior QA Engineer in the AI/ML team at Silicon Labs, you will play a pivotal role in defining and upholding quality standards for machine learning and deep learning models deployed on IoT edge devices. Based at our Hyderabad Software Centre of Excellence, you will design automated test frameworks, validate model performance under real-world conditions, and ensure seamless integration of AI technologies into next-generation IoT products. Meet the Team Youll be part of Silicon Labs newly established AI/ML SQA team, working at the forefront of innovation to deliver intelligent IoT solutions. The team collaborates closely with ML developers, DevOps, and product engineers across geographies to support the development, testing, and deployment of ML models and data pipelines. This team is responsible for building the foundation of quality assurance for ML models, enabling cutting-edge IoT products powered by artificial intelligence. Responsibilities : Develop and execute test strategies for machine learning, deep learning, and Tiny LLM models running on IoT edge devices. Validate model accuracy, robustness, and scalability under real-world IoT data conditions. Design automated frameworks to test data pipelines, feature extraction, inference performance, and edge/cloud integration. Ensure seamless integration of ML/DL modules into the IoT platform software stack (firmware, middleware, connectivity, and cloud APIs). Collaborate with ML developers to ensure models meet production-grade quality standards. Work with DevOps engineers to integrate ML model validation into CI/CD workflows. Requirements : Bachelors degree in Electrical Engineering or Computer Science (or equivalent combination of education and experience. 5+ Years of relevant industry experience Strong understanding of machine learning frameworks such as TensorFlow, PyTorch, scikit-learn. Experience in designing and executing automated test frameworks for ML/DL systems. Familiarity with DevOps tools like Docker, Kubernetes, Jenkins, GitLab CI. Exposure to ML model optimization for edge devices (e.g., TensorRT, OpenVINO, Edge TPU). Knowledge of MLOps practices, including model versioning and deployment workflows. Understanding of natural language processing and Tiny LLMs is a plus. Benefits & Perks At Silicon Labs, youll be part of a highly skilled team where every engineer makes a meaningful impact. We promote work-life balance and a welcoming, fun environment. Equity Rewards (RSUs) Employee Stock Purchase Plan (ESPP) Insurance plans with outpatient cover National Pension Scheme (NPS) Flexible work policy Childcare support Silicon Labs is an equal opportunity employer and values the diversity of our employees. Employment decisions are made on the basis of qualifications and job-related criteria without regard to race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status, or any other characteristic protected by applicable law. Show more Show less

About Entrupy Entrupy is a global technology company whose mission is to protect businesses, borders and consumers from transacting in counterfeit goods. Entrupy has developed a patented technology system which utilizes a combination of AI and computer vision to instantly identify and authenticate high value physical goods. Entrupy&aposs solutions serve business customers including leading luxury brands, retailers, e-commerce marketplaces and online resellers in over 60 countries. Entrupy is growing quickly with team members based in the US, India, Japan and Brazil. Entrupy&aposs solutions in market: Entrupy Luxury Authentication Entrupy Sneaker Authentication Entrupy Fingerprinting As we continue to build... We&aposre seeking curious, growth minded thinkers to help shape our vision, structures and systems; playing a key-role as we launch into our ambitious future. If you&aposre invigorated by our mission, values, and drive to change the world we&aposd love to have you apply. About the role We are seeking a Machine Learning Engineer with expertise in classical Computer Vision and Deep Learning to contribute to the development of advanced computer vision algorithms focused on item authentication and fingerprinting. In this role, you will drive the research, development, and deployment of state-of-the-art algorithms and models aimed at accurately determining the authenticity of items and assigning unique digital identities through image analysis. You will also oversee the optimization of image capture solutions on edge devices and ensure models are interpretable, scalable, and robust. As a senior team member, you will collaborate cross-functionally to define and deliver impactful projects, and contribute to thought leadership in the computer vision domain. Reports to: ML Manager Location: Bangalore, India (Hybrid) What You&aposll Do: Leadership & Strategy Define the technical roadmap for computer vision projects, aligning with business goals and customer needs. Mentor and provide technical guidance to junior engineers, fostering a culture of collaboration, innovation, and excellence. Advanced Research & Algorithm Development Design and develop cutting-edge computer vision models and algorithms for item authentication, fingerprinting, high-quality image capture and related applications. Lead innovation by staying ahead of trends in computer vision and machine learning, introducing novel techniques to improve model performance and scalability. Scalable Systems & Model Deployment Oversee the deployment of machine learning models in production, ensuring robustness, efficiency, and seamless integration into systems. Optimize algorithms for edge device deployment, focusing on latency, accuracy, and power consumption. Collaboration & Stakeholder Engagement Work closely with internal and external stakeholders to understand product requirements, define project scopes, and deliver actionable solutions. Collaborate with engineering teams to integrate CV models into broader system architectures while maintaining system reliability and performance. Operational Excellence Drive best practices in code quality, testing, and documentation. Review and refine code, ensuring modularity, reliability, and adherence to industry standards. Analyze production data to identify opportunities for continuous improvement in algorithms and systems. Who you are: Education: Bachelor&aposs or Master&aposs in Electronics, Physics, Computer Science, or related field. Experience: 4-7 years in computer vision and machine learning development. Core Expertise: Proficient in both traditional CV techniques (feature extraction, image processing) and deep learning methodologies. Advanced knowledge of Python and C++, with expertise in frameworks such as PyTorch, HuggingFace, OpenCV, and CUDA. Extensive experience in designing, training, and deploying CV models at scale, including distributed training and inference pipelines. Expertise in API design and deployment for ML model services such as FastAPI, TensorRT, Nvidia Triton, and Ray. Mathematical Proficiency: Strong foundation in probability, statistics, optimization, linear algebra, and geometry. EdgeML Experience: Skilled in deploying and optimizing models for iOS devices, with a focus on real-time data processing and resource efficiency. Collaboration & Communication: Ability to articulate technical concepts and collaborate in cross-functional, global teams. Ownership & Drive: A proactive approach, a high degree of ownership, and an eagerness to tackle complex challenges. What we offer Market competitive and pay equity-focused compensation structure Hybrid with Flexible work from anywhere for 4-8 weeks per year Generous time away, including company holidays, paid time off, sick time, parental leave, and more! Rich medical benefits and insurance coverage. The agency to innovate, directly impacting product features and customer experiences. Opportunity to be part of the core team in a growing setup. Opportunities to publish research, contribute to patents, and further develop CVML and edgeML expertise. We have had an incredible run so far and laying the foundation for a culture that is fast-paced, entrepreneurial, and rooted in passion, kindness, and positivity. We live by these values we hire by them, promote them, and celebrate them every day. If you are a visionary Computer Vision / ML engineer with extensive experience in crafting production-ready solutions, a passion for mentoring, and a desire to solve impactful challenges, we&aposd love to connect! Entrupy embraces a diversity of backgrounds and experiences and provides equal opportunity for all applicants and employees. We are dedicated to building a company that represents a variety of backgrounds, perspectives, and skills. We believe that the more inclusive we are, the better our work (and work environment) will be for everyone. Show more Show less

Ready to build the future with AI At Genpact, we don&rsquot just keep up with technology&mdashwe set the pace. 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, innovation-driven environment, love building and deploying cutting-edge AI solutions, and want to push the boundaries of what&rsquos possible, 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 Business Analyst , Data Scientist In this role, w e 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. 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 Lead AI-first transformation - Build and scale AI solutions that redefine industries Make an impact - Drive change for global enterprises and solve business challenges that matter Accelerate your career &mdashGain hands-on experience, world-class training, mentorship, and AI certifications to advance your skills Grow with the best - Learn from top engineers, data scientists, and AI experts in a dynamic, fast-moving workplace Committed to ethical AI - Work in an environment where governance, transparency, and security are at the core of everything we build 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 140,000+ coders, 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.

NVIDIA has continuously reinvented itself for over two decades. Our invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI - the new era of computing, positioning GPUs as the driving force behind intelligent applications in productivity, gaming, and creative fields, solidifying NVIDIA's position as the leading AI computing company. There is a growing emphasis on processing AI computations at the edge, closer to the source of data. This approach reduces latency, improves real-time processing, and addresses privacy concerns by minimizing the need for data sending to centralized servers. As technology continues to advance, we can expect client-side AI (local execution) to play a pivotal role in crafting the digital landscape. WinAI seeks a Senior Systems Software Engineer interested in solving client-side AI challenges on Windows PCs with limited resources. What You'll Be Doing: Partnering with NVIDIA software, research, architecture, and product teams to align strategies and technical needs for encouraging the ecosystem of AI on Windows RTX PCs. Collaborate closely with Microsoft to advance AI across critical domains-including graphics, web browsers, and edge devices-by driving innovation in technologies such as WindowsML, ONNX Runtime, and NVIDIA's proprietary libraries and driver stack. Improving performance on current and next-generation GPU architectures by conducting in-depth analysis and end-to-end optimization of AI models, data processing pipelines, and inference runtime features. Identifying, evaluating, and implementing compute and memory optimization techniques-such as quantization, distillation, and pruning-for large AI models fine-tuning and compressing models to fit edge devices. What We Need to See: Bachelor's, Master's, or PhD in Computer Science, Software Engineering, Mathematics, or a related field (or equivalent experience). Excellent C++ programming and debugging skills with a strong understanding of data structures and algorithms. 5+ years of experience with proficiency in AI inferencing pipelines and applications using ML/DL frameworks like ONNX RT, DirectML, PyTorch, Tensor RT. Strong analytical and problem-solving abilities, with the ability to multitask effectively in a dynamic environment. Outstanding written and oral communication skills enabling effective collaboration with management and engineering teams. Ways To Stand Out from The Crowd: Understanding modern techniques in Machine Learning, Deep Neural Networks, and Generative AI with relevant contributions to major open-source projects will be a plus. Consistent track record of delivering end-to-end products with geographically distributed teams in multinational product companies. Proficiency in lower-level system/GPU programming, CUDA, developing high-performance systems. Hands-on experience with building applications using APIs like ONNX RT, DirectML, DirectX, PyTorch, TensorRT, Vulkan. We're a top employer known for innovation and growth. We are an equal-opportunity employer and value diversity at our company. With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we would like to hear from you.

As a Senior Engineer - Perception Systems at Rebhu Computing, you will play a crucial role in leading the design and development of high-performance perception modules for next-generation robotic systems. Your responsibilities will encompass technical leadership, team and project support, engineering management, hiring, and team building. In terms of technical leadership, you will be tasked with architecting end-to-end perception systems, overseeing the integration of various perception modules with robotic platforms, and driving the optimization of real-time computer vision and sensor fusion stacks. Additionally, you will be expected to mentor junior engineers, collaborate with cross-functional teams, and ensure effective communication between internal stakeholders and clients. Setting standards for code quality, testing, and continuous integration, leading technical decision-making, and acting as a technical point of contact for clients and internal leadership are also part of your role. When it comes to hiring and team building, you will define role requirements, conduct technical interviews, and contribute to building a world-class perception team. Your expertise will help shape the engineering culture through thoughtful hiring and mentoring practices. To be successful in this role, you must have a minimum of 5 years of experience in computer vision, sensor fusion, or robotics perception, as well as proficiency in C++ and/or Python for real-time systems. Strong experience with frameworks like OpenCV, ROS, TensorRT, and hands-on experience deploying models on embedded or edge platforms are essential. A deep understanding of camera models, calibration, and real-time data processing is also required. Experience with SLAM, multi-sensor fusion, or 3D vision pipelines, familiarity with embedded Linux, GStreamer, or low-latency video pipelines, and prior leadership, mentoring, or hiring experience are considered nice-to-have qualifications. In return, Rebhu Computing offers you the opportunity to lead perception in mission-critical, real-world systems, a collaborative and intellectually vibrant work environment, as well as competitive compensation and performance-based incentives.,

Qualcomm India Private Limited is a leading technology innovator in the Engineering Group, specifically in Systems Engineering. As a Qualcomm Systems Engineer, you will be involved in researching, designing, developing, simulating, and validating systems-level software, hardware, architecture, algorithms, and solutions to drive the development of cutting-edge technology. Collaboration across functional teams is essential to meet and exceed system-level requirements and standards. To qualify for this role, you should possess a Bachelor's degree in Engineering, Information Systems, Computer Science, or related field with at least 8 years of experience in Systems Engineering. Alternatively, a Master's degree with 7+ years of experience or a Ph.D. with 6+ years of experience in the same field is also acceptable. Currently, Qualcomm is seeking a Principal AI/ML Engineer with expertise in model inference, optimization, debugging, and hardware acceleration. The role focuses on building efficient AI inference systems, debugging deep learning models, optimizing AI workloads for low latency, and accelerating deployment across various hardware platforms. In addition to hands-on engineering tasks, the role also involves cutting-edge research in efficient deep learning, model compression, quantization, and AI hardware-aware optimization techniques. The ideal candidate will collaborate with researchers, industry experts, and open-source communities to enhance AI performance continuously. The suitable candidate should have a minimum of 20 years of experience in AI/ML development, with a focus on model inference, optimization, debugging, and Python-based AI deployment. A Master's or Ph.D. in Computer Science, Machine Learning, or AI is preferred. Key Responsibilities of this role include Model Optimization & Quantization, AI Hardware Acceleration & Deployment, and AI Research & Innovation. The candidate should have expertise in optimizing deep learning models, familiarity with deep learning frameworks, proficiency in CUDA programming, and experience with various ML inference runtimes. Qualcomm encourages applicants from diverse backgrounds and is an equal opportunity employer. The company is committed to providing reasonable accommodations to individuals with disabilities during the hiring process. It is vital for all employees to adhere to applicable policies and procedures, including those related to confidentiality and security. Qualcomm does not accept unsolicited resumes or applications from staffing and recruiting agencies. For further information about this role, interested individuals may reach out to Qualcomm Careers.,

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.

The role requires you to design, develop, and maintain complex, high-performance, and scalable MLOps systems that interact with AI models and systems. You will collaborate with cross-functional teams, including data scientists, AI researchers, and AI/ML engineers, to comprehend requirements, define project scope, and ensure alignment with business goals. Your expertise will be crucial in selecting, evaluating, and implementing software technologies, tools, and frameworks within a cloud-native (Azure + AML) environment. Troubleshooting and resolving intricate software issues to ensure optimal performance and reliability when interfacing with AI/ML systems is an essential part of your responsibilities. Additionally, you will contribute to software development project planning and estimation, ensuring efficient resource allocation and timely solution delivery. Your role involves contributing to the development of continuous integration and continuous deployment (CI/CD) pipelines, high-performance data pipelines, storage systems, and data processing solutions. You will drive the integration of GenAI models, such as LLMs and foundation models, into production workflows, including overseeing orchestration and evaluation pipelines. Moreover, you will provide support for edge deployment use cases through model optimization, conversion (e.g., to ONNX, TFLite), and containerization for edge runtimes. Your contribution to creating and maintaining technical documentation, including design specifications, API documentation, data models, data flow diagrams, and user manuals, will be vital for effective communication within the team. **Required Qualifications:** - Bachelor's degree in software engineering/computer science or related discipline - Minimum of 6 years of experience in machine learning operations or software/platform development - Strong familiarity with Azure ML, Azure DevOps, Blob Storage, and containerized model deployments on Azure - Proficiency in programming languages commonly used in AI/ML, such as Python, R, or C++ - Experience with Azure cloud platform, machine learning services, and industry best practices **Preferred Qualifications:** - Experience with machine learning frameworks like TensorFlow, PyTorch, or Keras - Familiarity with version control systems like Git and CI/CD tools such as Jenkins, GitLab CI/CD, or Azure DevOps - Knowledge of containerization technologies such as Docker and Kubernetes, along with infrastructure-as-code tools like Terraform or Azure Resource Manager (ARM) templates - Exposure to Generative AI workflows, including prompt engineering, LLM fine-tuning, or retrieval-augmented generation (RAG) - Understanding of GenAI frameworks like LangChain, LlamaIndex, Hugging Face Transformers, and OpenAI API integration - Experience in deploying optimized models on edge devices using ONNX Runtime, TensorRT, OpenVINO, or TFLite - Hands-on experience with monitoring LLM outputs, feedback loops, or LLMOps best practices - Familiarity with edge inference hardware such as NVIDIA Jetson, Intel Movidius, or ARM Cortex-A/NPU devices This is a permanent position requiring in-person work.,

Description By applying to this position, your application will be considered for all locations we hire for in the United States. Annapurna Labs designs silicon and software that accelerates innovation. Customers choose us to create cloud solutions that solve challenges that were unimaginable a short time agoeven yesterday. Our custom chips, accelerators, and software stacks enable us to take on technical challenges that have never been seen before, and deliver results that help our customers change the world. Role AWS Neuron is the complete software stack for the AWS Trainium (Trn1/Trn2) and Inferentia (Inf1/Inf2) our cloud-scale Machine Learning accelerators. This role is for a Machine Learning Engineer on one of our AWS Neuron teams: The ML Distributed Training team works side by side with chip architects, compiler engineers and runtime engineers to create, build and tune distributed training solutions with Trainium instances. Experience with training these large models using Python is a must. FSDP (Fully-Sharded Data Parallel), Deepspeed, Nemo and other distributed training libraries are central to this and extending all of this for the Neuron based system is key. ML?Frameworks partners with compiler, runtime, and research experts to make AWS?Trainium and?Inferentia feel native inside the tools builders already lovePyTorch, JAX, and the rapidly evolving vLLM ecosystem. By weaving Neuron?SDK deep into these frameworks, optimizing operators, and crafting targeted extensions, we unlock every teraflop of Annapurnas AI chips for both training and lightning?fast inference. Beyond kernels, we shape next?generation serving by upstreaming new features and driving scalable deployments with vLLM, Triton, and TensorRTturning breakthrough ideas into production?ready AI for millions of customers. The ML Inference team collaborates closely with hardware designers, software optimization experts, and systems engineers to develop and optimize high-performance inference solutions for Inferentia chips. Proficiency in deploying and optimizing ML models for inference using frameworks like TensorFlow, PyTorch, and ONNX is essential. The team focuses on techniques such as quantization, pruning, and model compression to enhance inference speed and efficiency. Adapting and extending popular inference libraries and tools for Neuron-based systems is a key aspect of their work. Key job responsibilities You&aposll join one of our core ML teams - Frameworks, Distributed Training, or Inference - to enhance machine learning capabilities on AWS&aposs specialized AI hardware. Your responsibilities will include improving PyTorch and JAX for distributed training on Trainium chips, optimizing ML models for efficient inference on Inferentia processors, and collaborating with compiler and runtime teams to maximize hardware performance. You&aposll also develop and integrate new features in ML frameworks to support AWS AI services. We seek candidates with strong programming skills, eagerness to learn complex systems, and basic ML knowledge. This role offers growth opportunities in ML infrastructure, bridging the gap between frameworks, distributed systems, and hardware acceleration. About The Team Annapurna Labs was a startup company acquired by AWS in 2015, and is now fully integrated. If AWS is an infrastructure company, then think Annapurna Labs as the infrastructure provider of AWS. Our org covers multiple disciplines including silicon engineering, hardware design and verification, software, and operations. AWS Nitro, ENA, EFA, Graviton and F1 EC2 Instances, AWS Neuron, Inferentia and Trainium ML Accelerators, and in storage with scalable NVMe, Basic Qualifications To qualify, applicants should have earned (or will earn) a Bachelors or Masters degree between December 2022 and September 2025. Working knowledge of C++ and Python Experience with ML frameworks, particularly PyTorch, Jax, and/or vLLM Understanding of parallel computing concepts and CUDA programming Preferred Qualifications Experience in using analytical tools, such as Tableau, Qlikview, QuickSight Experience in building and driving adoption of new tools Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region youre applying in isnt listed, please contact your Recruiting Partner. Company - Annapurna Labs (U.S.) Inc. Job ID: A3029797 Show more Show less

The Computer Vision Engineer position in Hyderabad requires someone with 3 to 5 years of experience in developing, implementing, and optimizing deep learning models. As a Deep Learning Engineer, you will be responsible for driving advanced AI solutions across various industries by leveraging your expertise in neural networks, data processing, and model deployment. Your main responsibilities will include owning the product development milestones, ensuring delivery to the architecture, and identifying challenges. You will drive innovation in the product, cater to successful initiatives, and establish engineering best practices for core product development teams within the company. In this role, you will be involved in developing, porting, and optimizing computer vision algorithms and data structures on proprietary cores. You will also engage in research and development efforts focused on advanced product-critical computer vision components, such as feature extraction, tracking objects, and sensor calibration. Solid programming skills in Python and C/C++, as well as experience with TensorFlow, PyTorch, ONNX, MXNet, Caffe, OpenCV, Keras, and various neural networks, frameworks, and platforms are essential. Previous exposure to GPU computing, HPC, cloud services like AWS/Azure/Google, and NoSQL databases will be beneficial. You should have hands-on experience in deploying efficient Computer Vision products, implementing research papers, using dockerized containers with microservices, and optimizing models for TensorRT. Familiarity with NVIDIA Jetson Nano, TX1, TX2, Xavier NX, AGX Xavier, Raspberry Pi, and edge devices is required. Understanding of computer vision concepts like photogrammetry, multi-view geometry, visual SLAM, detection and recognition, and 3D reconstruction is crucial. You will need to write maintainable, reusable code, leverage test-driven principles, and develop high-quality computer vision and machine learning modules. Experience with object detection, tracking, classification, recognition, scene understanding, and deep neural networks is important. Furthermore, knowledge of image classification, object detection, and semantic segmentation using deep learning algorithms is desirable. You should be able to evaluate and advise on new technologies, vendors, products, and competitors. Initiative, independence, teamwork, and hands-on technical expertise are key attributes for this role. If you are ready to contribute to cutting-edge AI solutions and drive innovation in computer vision, apply now and join our awesome squad.,

You will play a pivotal role in the design and implementation of cutting-edge GPU computers optimized for demanding deep learning, high-performance computing, and computationally intensive workloads. Your expertise will be essential in identifying architectural enhancements and innovative approaches to accelerate our deep learning models. Addressing strategic challenges related to compute, networking, and storage design for large-scale, high-performance workloads will be a key responsibility. Additionally, you will contribute to effective resource utilization in a heterogeneous computing environment, evolve our cloud strategy, perform capacity modeling, and plan for growth across our products and services. As an architect, you are tasked with translating business requirements pertaining to AI-ML algorithms into a comprehensive set of product objectives encompassing workload scenarios, end user expectations, compute infrastructure, and execution timelines. This translation should culminate in a plan to operationalize the algorithms efficiently. Furthermore, you will be responsible for benchmarking and optimizing Computer Vision Algorithms and Hardware Accelerators based on performance and quality KPIs. Your role will involve fine-tuning algorithms for optimal performance on GPU tensor cores and collaborating with cross-functional teams to streamline workflows spanning data curation, training, optimization, and deployment. Providing technical leadership and expertise for project deliverables is a core aspect of this position, along with leading, mentoring, and managing the technical team to ensure successful outcomes. Your contributions will be instrumental in driving innovation and achieving project milestones effectively. Key Qualifications: - Possess an MS or PhD in Computer Science, Electrical Engineering, or a related field. - Demonstrated expertise in deploying complex deep learning architectures. - Minimum of 5 years of relevant experience in areas such as Machine Learning (with a focus on Deep Neural Networks), DNN adaptation and training, code development for DNN training frameworks (e.g., Caffe, TensorFlow, Torch), numerical analysis, performance analysis, model compression, optimization, and computer architecture. - Strong proficiency in data structures, algorithms, and C/C++ programming. - Hands-on experience with PyTorch, TensorRT, CuDNN, GPU computing (CUDA, OpenCL, OpenACC), and HPC (MPI, OpenMP). - Thorough understanding of container technologies like Docker, Singularity, Shifter, Charliecloud. - Proficient in Python programming, bash scripting, and operating systems including Windows, Ubuntu, and Centos. - Excellent communication, collaboration, and problem-solving skills. Good To Have: - Practical experience with HPC cluster job schedulers such as Kubernetes, SLURM, LSF. - Familiarity with cloud computing architectures. - Hands-on exposure to Software Defined Networking and HPC cluster networking. - Working knowledge of cluster configuration management tools like Ansible, Puppet, Salt. - Understanding of fast, distributed storage systems and Linux file systems for HPC workloads. This role offers an exciting opportunity to contribute to cutting-edge technology solutions and make a significant impact in the field of deep learning and high-performance computing. If you are a self-motivated individual with a passion for innovation and a track record of delivering results, we encourage you to apply.,

Joining Gadgeon offers a dynamic and rewarding career experience that fosters both personal and professional growth. Our collaborative culture encourages innovation, empowering team members to contribute their ideas and expertise to cutting-edge projects. As a Computer Vision & Edge AI Engineer at Gadgeon, you will be responsible for developing AI solutions for visual understanding, document parsing, and multimodal processing. In this role, you will enable new use cases beyond text by integrating image, OCR, and edge-deployable AI capabilities. Key Duties/ Responsibilities: - Develop OCR and image processing pipelines using tools like OpenCV, Tesseract, or AWS Textract. - Train and fine-tune visual models such as YOLOv8, SAM, CLIP for internal Proof of Concepts (PoCs). - Integrate visual AI modules into end-to-end workflows used by LLMs or agents. - Optimize models for edge deployments using ONNX, TensorRT, or TFLite. - Collaborate with backend and AI teams for data structure alignment. Leadership Skills: - Self-driven in visual AI exploration. - Effective in prototyping and cross-functional collaboration. - Ability to demonstrate impact through PoCs. Required Technical Skills: - Proficiency in OpenCV, YOLOv8, SAM, BLIP2, Tesseract. - Experience with ONNX, TensorRT, AWS Panorama, Jetson Nano. - Strong programming skills in Python, PyTorch/TensorFlow, edge deployment toolchains. - Familiarity with visual AI stacks like YOLOv8, SAM, CLIP, or equivalent. - Capability to structure visual outputs for downstream agent or LLM processing. Qualification: - Bachelors or Master's degree in Computer Science, AI/ML, Data Science, or related fields. Experience: - Minimum of 4 years of relevant experience in the field.,