Lead - Computer Vision Scientist-AI Labs

Purpose/Objective

The Lead - Computer Vision AI Labs translates strategic AI initiatives into executional workflows, leading a team of AI engineers, data scientists, and computer vision specialists. The Lead oversees the development of AI-powered image analytics, model deployment, and performance optimization, ensuring AI solutions align with business objectives. The role also involves collaborating with cross-functional teams to scale AI adoption and enhance real-time decision intelligence across enterprise operations.

Key Responsibilities of Role

Lead-Computer Vision Scientist-AI Labs AI Model Development & Deployment: Deliver high-performing AI-driven computer vision models by leading their design, training, and deployment, ensuring accuracy exceeds predefined benchmarks. Implement and optimize real-time image processing pipelines, utilizing TensorFlow, PyTorch, and OpenCV, to achieve low-latency, high-precision performance. Enable AI-driven automation and industrial intelligence by successfully integrating computer vision models into robotics, manufacturing analytics, and process automation systems. Improve AI model adaptability and accuracy by establishing automated feedback loops, retraining mechanisms, and performance optimization frameworks. Enhance AI scalability and deployment efficiency by refining inference pipelines for cloud (AWS, Azure, GCP) and edge computing platforms, reducing processing time. Image Analytics & AI Optimization: Develop and deploy advanced image recognition solutions for object detection, OCR, and anomaly detection, ensuring X% increase in recognition accuracy. Enhance real-time object classification and decision-making by incorporating LiDAR, RGB-D, and thermal imaging-based AI models into production workflows. Reduce AI model inference time and error rates by refining deep learning architectures for low-latency and high-precision analytics in dynamic environments. Expand AI-driven automation in IoT and surveillance ecosystems by embedding optimized vision models into smart monitoring and predictive maintenance systems. AI Governance, Risk, and Compliance: Ensure full compliance with AI ethics, privacy laws, and industry best practices by implementing standardized governance frameworks for AI models. Establish and enforce model validation processes to eliminate bias, improve interpretability, and enhance explainability by X% across AI deployments. Strengthen AI security and reliability by developing risk mitigation strategies, anomaly detection protocols, and automated compliance audits for AI models. Improve model lifecycle management by conducting quarterly audits and performance evaluations, leading to an increase in AI deployment success rates. Cross-Functional Collaboration & Business Integration: Integrate AI-driven computer vision models into enterprise applications by collaborating with data scientists, software engineers, and business leaders to deliver AI-driven insights. Accelerate AI adoption across teams by driving hands-on experimentation, model validation, and successful deployment of AI use cases in business-critical functions. Optimize AI deployment pipelines by working with DevOps teams to automate model release cycles through CI CD integrations, reducing deployment time Enhance AI-powered decision intelligence by defining clear business use cases, technical requirements, and impact metrics with product managers and stakeholders. Team Leadership & Capability Building: Develop a high-performing AI team by managing, mentoring, and training AI engineers, data scientists, and image processing specialists in deep learning advancements. Enhance team capabilities by leading structured training programs, ensuring X% improvement in AI proficiency across key technical domains. Drive research and innovation in AI labs, leading to the development of X new AI solutions per year, enhancing business decision-making. Expand AI expertise in the organization by leading hiring, workforce planning, and talent acquisition strategies to build a best-in-class AI R&D team. Key Stakeholders - Internal Chief Technology Officer (CTO) Chief Data Officer (CDO) AI & Data Science Teams Product & Engineering Teams Business Units & Operations Teams Cybersecurity & Compliance Teams Key Stakeholders - External AI Research Institutes & Universities Regulatory Authorities (GDPR, AI Act, ISO AI Compliance) Industry Partners & AI Consortiums Technology Vendors & Cloud Providers (AWS, GCP, Azure)

Technical Competencies

AI Deployment, Infrastructure & Model Scaling-SDIL,AI Governance, Compliance & Ethical AI-SDIL,AI Model Development & Optimization-SDIL,AI Product & Solution Development-SDIL,AI Research, Experimentation & Emerging Technologies-SDIL,AI Strategy & Enterprise Transformation-SDIL,AI-Driven Enterprise Transformation & Operational Excellence-SDIL,Cross-Functional Collaboration & AI Integration-SDIL

Qualifications and Experience

Educational Qualification: Master’s Ph.D. in AI, Computer Science, Machine Learning, or Computer Vision (Preferred from ISI IISc IIT Top AI Universities) MBA or Business Leadership Programs (Preferred for Business Strategy Integration) Certification: (Preferred but Not Mandatory) AWS GCP Azure AI & Machine Learning Certification Deep Learning Specialization (Coursera, Stanford, Andrew Ng’s DL Program) Computer Vision Nanodegree (Udacity, MIT AI) AI Governance & Ethics Certification (ISO, AI Regulatory Frameworks) DevOps for AI & MLOps Certification (Google, Kubernetes, TensorFlow Extended – TFX) Work Experience (Range of years): 10+ years