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Tier-1 brand, common Data Scientist title, mid-level seniority, and metro office drive high competition.
Specialized GenAI and MLOps focus is transferable across industries but requires strong ML domain expertise.
Extensive mandatory GenAI, MLOps, cloud, and deep learning stack increases filtering despite no explicit years.
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Design, develop, and deploy advanced Generative AI models including LLMs and diffusion models for enterprise applications.
Fine-tune pretrained models for domain-specific tasks and integrate them into production via APIs and microservices.
Optimize inference pipelines for performance, scalability, and low latency using distributed processing; ensure compliance with data privacy, security, and ethical AI standards.
Proficiency in Python and Data Science mandatory.
Experience with deep learning frameworks such as TensorFlow or PyTorch.
Familiarity with cloud platforms (AWS, Azure, GCP), containerization (Docker, Kubernetes), and CI/CD pipelines.
Work Experience Required: Not explicitly mentioned in the JD.
Experience architecting and deploying GenAI capabilities into business workflows and collaborating with product teams.
Skilled in MLOps, data pipelines, ETL processes, and performance tuning for AI model deployment.
Knowledge of prompt engineering, Reinforcement Learning with Human Feedback (RLHF), multimodal AI, and experience in banking domain GenAI use case implementations is a plus.