Lead Computer Vision Engineer

IT Delivery • Primary skills:Technology->Artificial Intelligence->Computer Vision,Technology->Machine Learning->Azure ML • Knowledge of more than one technology • Basics of Architecture and Design fundamentals • Knowledge of Testing tools • Knowledge of agile methodologies • Understanding of Project life cycle activities on development and maintenance projects • Understanding of one or more Estimation methodologies, Knowledge of Quality processes • Basics of business domain to understand the business requirements • Analytical abilities, Strong Technical Skills, Good communication skills • Good understanding of the technology and domain • Ability to demonstrate a sound understanding of software quality assurance principles, SOLID design principles and modelling methods • Awareness of latest technologies and trends • Excellent problem solving, analytical and debugging skills 1. Lead the end-to-end design and development of computer vision solutions, from problem definition and data understanding to model deployment and monitoring. 2. Architect and implement robust pipelines for image and video processing, feature extraction, and model inference using modern computer vision techniques. 3. Leverage and integrate Azure-based computer vision capabilities 4. Collaborate with product managers and stakeholders to translate business requirements into clear technical specifications and solution designs. 5. Provide technical leadership to a small team of engineers, including code reviews, design guidance, and mentoring on best practices in computer vision and cloud implementation. 6. Optimize models and pipelines for performance, scalability, and reliability in production environments, including latency, throughput, and cost considerations. 7. Establish and promote coding standards, documentation practices, and reusable components for computer vision projects. 8. Evaluate new algorithms, frameworks, and Azure services relevant to computer vision, and drive their adoption where beneficial. 9. Work closely with DevOps/cloud teams to ensure secure, resilient, and maintainable deployments of vision services on Azure. 10. Troubleshoot production issues related to computer vision components and drive continuous improvement through root-cause analysis and iterative enhancements.