4 Openvino Jobs

ML Engineer Position - ML - Engineer Experience - 7+ yrs Client Name - Honeywell International Ltd Payroll Company - Bramha Tech CTC - As per industry norms Job Location - Pune Notice Period - Immediate/ serving Notice Objectives of this role • Design and develop efficient computer vision applications for security and surveillance domain. • Develop computer vision applications and algorithms for deploying on low power embedded devices. • Collaborate with firmware engineers, front-end engineers, QA Engineers and architects on production systems and applications. • Identify differences in data distribution that could potentially affect model performance in real world applications • Ensure algorithms generate accurate predictions. • Stay up to date with developments in the machine learning industry. • Do Data versioning as well as model versioning of the collected data and developed models. Skills and qualifications • Extensive math and computer skills, with a deep understanding of probability, statistics, and algorithms. • Familiarity with deploying deep learning models on low power embedded devices. • Good knowledge of programming with C and C++ is must. • Proven record of working with AI Accelerators, NPU and quantization frameworks like OpenVINO or Neuralmagic. • In-depth knowledge of TF or PyTorch. • Familiarity of ArmNN, Kendryte NNcase, Maix Sipeed or RKNN toolkits. • Good knowledge of version control systems like Git, Azure Repos. • Familiarity with data structures, data modeling, and software architecture. • Impeccable analytical and problem-solving skills Preferred qualifications • Proven experience as a machine learning engineer or similar role • Bachelors degree (or equivalent) in computer science, mathematics, or related field

Role Summary: As the Edge AI Lead , you will oversee the design, optimization, and deployment of AI and machine learning models on edge devices, including NVIDIA Jetson, STM microcontrollers, and other embedded platforms. This role requires expertise in real-time operating systems (RTOS), the Robot Operating System (ROS), and hardware accelerators such as NPUs, TPUs, CPUs, and GPUs. You will lead a team to deliver high-performance, low-latency AI solutions and collaborate closely with robotics, embedded, and mechanical teams to create efficient, real-time autonomous systems. Core Responsibilities: 1. Design and Development of Edge AI Models Lead Edge AI Model Development: Drive the design and development of AI models optimized for edge devices and real-time processing. Focus on applications like object detection, SLAM (Simultaneous Localization and Mapping), sensor fusion, and predictive maintenance. Hardware-Aware Model Optimization: Develop models with an understanding of the constraints and capabilities of different hardware accelerators, including NPUs (Neural Processing Units), TPUs (Tensor Processing Units), CPUs, and GPUs. Optimize models through quantization, pruning, and knowledge distillation to fit the processing capabilities of edge devices. Integration with RTOS and ROS: Ensure models are compatible with real-time operating systems (RTOS) for reliable, time-sensitive applications. Leverage ROS for seamless integration with robotic systems, enabling efficient communication between AI models and control systems. 2. Deployment and Integration of Edge AI Models Edge Device Deployment: Lead the deployment of AI models on edge devices, including platforms like NVIDIA Jetson, STM32 microcontrollers, ARM Cortex, and Qualcomm Snapdragon. Ensure seamless integration with RTOS and sensor interfaces for reliable real-time processing. Coordinate with Robotics and Control Teams: Collaborate with robotics and embedded control teams to ensure AI models are effectively integrated within robotic platforms. Address challenges related to sensor placement, communication protocols, and power distribution. Optimize Data Flow with ROS and RTOS: Ensure AI models are well-integrated with ROS for real-time data exchange and with RTOS for deterministic performance. Optimize communication pathways between sensors, processors, and AI models for smooth data flow. 3. Edge AI Pipeline and MLOps Implement MLOps for Edge AI: Develop MLOps pipelines specifically tailored for edge deployment, including CI/CD, automated testing, and performance monitoring. Ensure models can be continuously updated, retrained, and redeployed based on operational data. Model Lifecycle Management: Manage model versioning, deployment, and rollback strategies to ensure reliable, up-to-date models are deployed on edge devices. Develop feedback loops to monitor model performance and enable continuous improvement. Real-Time Monitoring and Troubleshooting: Implement monitoring tools to track metrics such as latency, accuracy, and resource usage in real time. Develop protocols for troubleshooting issues to maintain reliability and performance in production environments. 4. Hardware Acceleration and Performance Optimization Leverage Hardware Accelerators: Use tools like NVIDIA TensorRT, CUDA, OpenVINO, and Qualcomm AI Stack to maximize performance on GPUs, TPUs, NPUs, and other hardware accelerators. Optimize AI inference to take full advantage of the specific architecture of each edge device. Low-Level Programming for Efficiency: Utilize low-level programming languages like C, C++, and assembly to optimize code execution on embedded systems. Implement custom kernels and optimize memory usage to improve performance on constrained hardware. Energy and Power Optimization: Focus on energy-efficient model deployment to maximize battery life and reduce power consumption, particularly for mobile and autonomous robots. Apply power management strategies to balance performance and energy efficiency. 5. Leadership and Team Management Lead and Mentor the Edge AI Engineering Team: Build and guide a team of Edge AI engineers, providing technical direction, mentorship, and hands-on support. Set clear performance expectations, provide regular feedback, and support professional growth. Project and Resource Management: Oversee project timelines, resource allocation, and task prioritization for the Edge AI team. Ensure alignment with broader organizational goals and coordinate with cross-functional teams to achieve project milestones. Foster a Culture of Innovation and Technical Excellence: Promote a culture of continuous learning, experimentation, and technical rigor within the team. Encourage knowledge-sharing and collaboration to solve complex edge AI challenges. 6. Collaboration with Cross-Functional Teams Work Closely with Embedded Systems and Software Teams: Collaborate with embedded systems and software engineers to ensure compatibility of AI models with embedded platforms, RTOS, and other system software. Address integration challenges related to memory constraints and real-time requirements. Coordinate with Robotics, Data, and Mechanical Teams: Work closely with robotics engineers, data scientists, and mechanical engineers to align on data needs, model integration, and design constraints. Ensure that Edge AI solutions support and enhance the capabilities of the robotic systems. Align with Head Robotics and AI Software Leadership: Collaborate with the Head Robotics and AGI/DL Software Head to ensure Edge AI development aligns with broader AI and robotics objectives. Ensure edge solutions support real-time perception, decision-making, and autonomous functionality. 7. Innovation and Research in Edge AI and Embedded Systems Explore Emerging Edge AI Technologies: Stay current with advancements in edge AI hardware, software, and frameworks, including low-power inference engines, neural accelerators, and lightweight edge computing frameworks. Assess and implement new technologies to enhance performance and efficiency. Lead R&D Projects on Edge AI Challenges: Initiate research projects to address unique edge AI challenges such as low-power image recognition, real-time SLAM, and multi-sensor fusion on edge devices. Lead proof-of-concept projects to validate new approaches and methodologies. Contribute to Open-Source and Industry Engagement: Encourage team participation in open-source projects and engagement with the AI/ML community. Promote contributions to Edge AI tools, libraries, and frameworks to stay connected with industry trends and drive community-driven innovation. 8. Compliance, Safety, and Quality Assurance Ensure Safety and Compliance Standards: Work closely with quality and compliance teams to ensure that Edge AI models meet industry safety standards, particularly for autonomous and industrial applications. Quality Control and Documentation: Implement quality control processes for Edge AI models, ensuring reliability, consistency, and long-term performance. Maintain comprehensive documentation on deployment protocols, optimization techniques, and troubleshooting procedures. Develop and Enforce Best Practices: Create and maintain best practices for edge AI development, deployment, and maintenance. Ensure protocols are well-documented, accessible, and regularly updated. Required Qualifications: Education: Bachelor's or Master's degree in Computer Science, Electrical Engineering, Robotics, or a related field. Advanced degrees or certifications in AI, embedded systems, or robotics are preferred. Experience: 8+ years of experience in AI, machine learning, or embedded systems, with at least 3 years in edge computing or embedded AI. Demonstrated experience deploying and optimizing AI/ML models on edge devices, with a focus on low-latency, high-performance environments. Strong experience in leading teams and managing projects involving edge AI, embedded systems, or robotics. Technical Skills: Machine Learning and AI: Proficiency in ML/DL frameworks (e.g., TensorFlow Lite, PyTorch, ONNX) with extensive experience in model optimization for edge devices. Real-Time Systems (RTOS) and ROS: Experience working with real-time operating systems (RTOS) and the Robot Operating System (ROS) to enable reliable real-time processing and seamless communication in robotic systems. Hardware Acceleration and Low-Level Programming: Expertise in using tools like TensorRT, CUDA, OpenVINO, and low-level programming languages (e.g., C, C++) to optimize AI inference on hardware accelerators (NPU, TPU, CPU, GPU). Embedded Systems and Edge Platforms: Deep knowledge of edge computing platforms such as NVIDIA Jetson, STM32 microcontrollers, ARM Cortex, and Qualcomm Snapdragon. Understanding of RTOS, middleware, and embedded programming. Preferred Qualifications: Robotics and Autonomous Systems Knowledge: Understanding of robotics control systems, perception systems, and integration of AI within autonomous systems. Energy-Efficient AI: Experience with optimizing AI models for low-power applications, particularly for mobile and autonomous robotics where power consumption is critical. Project Management and Agile Practices: Familiarity with Agile methodologies and experience in project management to oversee complex, cross-functional projects effectively. Open-Source and Community Engagement: A track record of contributions to open-source AI/ML or embedded systems projects and active participation in the Edge AI community.

Role: Senior Solution Architect Computer Vision Experience: 6+ years Role Overview: The Senior Solution Architect will be responsible for designing, developing, and deploying large-scale, real-time vision solutions for object detection, pattern recognition, behavior analysis, and intelligence augmentation. This role demands a deep technical understanding of computer vision, machine learning, and AI infrastructure, combined with strong leadership capabilities. The architect will collaborate with cross-functional teams to translate business needs into robust technical solutions while ensuring efficiency, scalability, and innovation. Key Responsibilities: 1. Solution Architecture & Technical Leadership: Design and implement scalable, high-performance computer vision solutions for complex business challenges. Architect real-time image and video processing pipelines , integrating technologies for object detection, tracking, and behavior analysis. Develop fault-tolerant, distributed systems using state-of-the-art deep learning and image processing techniques. Drive end-to-end system optimization , including data pipelines, model inference efficiency, and GPU utilization . Evaluate and integrate cutting-edge AI frameworks, edge computing strategies, and cloud-based vision services . 2. Project Leadership & Execution: Define technical strategy and roadmap for delivering vision-based AI solutions. Own end-to-end project execution , from requirement gathering to deployment. Develop comprehensive solution blueprints, architecture documentation, and execution plans . Ensure alignment with business goals, cost-effectiveness, and system reliability . Set and manage stakeholder expectations , ensuring timely and successful project delivery. 3. Collaboration & Cross-Functional Engagement: Work closely with AI research teams, data engineers, software engineers, and product managers to align technical designs with business objectives. Collaborate with cloud infrastructure, DevOps, and MLOps teams to ensure smooth deployment and monitoring of vision pipelines. Partner with hardware teams for optimizing computer vision models for edge devices and embedded systems. 4. Mentorship & Team Development: Mentor and train junior and mid-level computer vision engineers to build strong technical capabilities. Establish and enforce best practices for AI model development, deployment, and monitoring . Foster a culture of innovation, knowledge sharing, and technical excellence within the team. Skills & Qualifications: Technical Expertise: Strong proficiency in Python and Linux , with expertise in writing optimized, fault-tolerant code . Deep understanding of image processing and computer vision techniques. Experience with libraries such as OpenCV, NVIDIA CUDA, Intel OpenVINO, TensorFlow, PyTorch, and NumPy . Expertise in machine learning, deep learning, and AI model optimization for real-time inference. Strong knowledge of MLOps, model monitoring, and lifecycle management . Experience with edge AI deployment and optimizations for embedded systems (Jetson, TPU, FPGA, etc.) . Soft Skills: Ability to translate complex business challenges into scalable AI solutions . Strong communication and collaboration skills across technical and non-technical teams. Proven leadership skills , including mentoring, team management, and decision-making. Innovative mindset , with a passion for staying ahead of the curve in AI and vision technologies. Preferred Experience: Prior experience in large-scale vision automation solutions , ideally in fast-paced startup environments . Track record of successfully architecting and deploying computer vision solutions from concept to production . Experience with edge computing, federated learning, and privacy-preserving AI techniques . Contributions to open-source AI/computer vision projects or relevant publications in the field.

Responsibilities Develop and deploy computer vision algorithms and deep learning models for diverse problems. Design and implement computer vision models using state-of-the-art techniques and frameworks. Explore and analyze unstructured data like images through image processing. Analyze, evaluate and optimize existing computer vision systems to improve performance and accuracy. Test and validate computer vision code and models, ensuring robustness and reliability. Research and implement new computer vision technologies to stay at the forefront of the field. Collaborate with cross-functional teams to develop innovative solutions that meet project requirements. Monitor the performance and accuracy of computer vision models, making necessary adjustments and improvements. Maintain and update computer vision systems to ensure their continued functionality and relevance. Provide technical support and guidance to team members and customers using computer vision systems. Requirements 3 - 5 years of experience as a Computer Vision Engineer. Bachelor's degree in Computer Science, or a related field. Proven experience in developing computer vision systems, including hands-on implementation and deployment. Strong knowledge of computer vision algorithms, libraries and tools, such as OpenCV, TensorFlow, PyTorch, Keras, NumPy, scikit-image, PIL, Matplotlib, Seaborn, etc. Familiarity with tools and libraries commonly used in computer vision projects such as CUDA, OpenCL, OpenGL. Expertise in various computer vision projects, including object detection, image classification, text detection & OCR, face detection, generative models, video analytics, object tracking and model compression/optimization. Knowledge of runtime AI frameworks like ONNX, TensorRT, OpenVINO. Experience in cloud platforms (AWS, Azure), Docker, Kubernetes and GitHub. Experience in training models through GPU computing or on the cloud. Familiarity with machine learning and deep learning concepts and frameworks. Excellent problem-solving skills and the ability to think analytically. Good written and verbal communication skills for effectively communicating with the team and ability to present information to varied technical and non-technical audiences. Ability to work independently and in a fast-paced environment and also be able to work in a team when required. Desired Candidate Profile Experience: 3 - 5 years Location: Bangalore/Coimbatore Qualification: Computer Science or a related field Job Type: Full-Time, Permanent Schedule: Day Shift, Monday to Friday Workplace Type: On-site (Work from Office) Notice Period: Immediate