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Tier-1 employer, metro location, and mid-level technical role increase competition despite niche specialization.
Advanced generative-CV research skills are transferable but favor ML/vision-heavy industries and research teams.
Explicit Ph.D./M.S. requirement plus mandatory 3+ years and specific ML/CV stack makes filtering strict.
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Advance and implement Generative AI and deep learning techniques for industrial computer vision applications, focusing on tasks like synthetic data generation, defect detection, and anomaly detection.
Develop, deploy, and monitor robust computer vision models addressing challenges like limited data, data drift, and rare event detection across the full ML lifecycle.
Translate research into scalable prototypes and production-ready AI components collaborating cross-functionally with global teams for integration and impact.
Ph.D., M.S., or M.Tech in Computer Science, AI, Machine Learning, Electrical/Electronics Engineering, Robotics, Applied Math, or related fields from reputed institutes.
Minimum 3 years of applied professional or research experience in computer vision, deep learning, Generative AI, or related domains.
Strong proficiency in Python and PyTorch, with hands-on experience in advanced neural architectures including CNNs, transformers, and Generative AI methods like diffusion models and GANs.
Experience in model development for tasks such as image classification, defect and anomaly detection, with knowledge of model evaluation metrics and monitoring techniques.
Practitioner experienced in bridging advanced AI research with industrial computer vision applications, especially in synthetic data generation and robust model deployment.
Demonstrated ability to design rigorous experiments, benchmark models, and interpret empirical results to optimize performance in real-world scenarios.
Hands-on with scalable AI lifecycle practices, collaborating effectively across interdisciplinary and geographically distributed teams to deliver production-level AI solutions.