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Strong employer brand and metro location increase competition, but senior specialized ML/LLM focus limits applicant density.
Requires deep ML/LLM, LLMOps, and regulated-finance domain experience, so background transferability is low and domain-sensitive.
Explicit seniority range, mandatory deep ML/LLM experience, and leadership requirements create high shortlisting rigidity.
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Lead and define the strategic roadmap for enterprise-wide AI/ML and generative AI (LLM) adoption aligned with business goals.
Architect, develop, and oversee large-scale LLM-based AI solutions including prompt engineering, fine-tuning, embeddings, vector search, and RAG integration.
Build and manage high-performing data science teams while ensuring model governance, Responsible AI compliance, and operational MLOps/LLMOps practices.
Bachelor’s, BTech, or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or related quantitative field.
12-17 years overall IT experience with 8+ years in data science, ML, advanced analytics; 2+ years in leading LLM/generative AI solution development and deployment at enterprise scale.
Proficient in Python and AI/ML frameworks (PyTorch, TensorFlow, Scikit-learn, Keras).
Hands-on experience with LLMs, NLP, prompt engineering, embeddings, vector databases, RAG, knowledge graphs, and cloud AI/ML platforms (Azure and/or AWS).
Demonstrated leadership in building and managing data science teams focused on enterprise AI/ML and generative AI solutions.
Strong strategic and operational experience integrating AI systems with business units and governance frameworks in large organizations.
Hands-on expertise implementing scalable production-grade AI models with strong emphasis on Responsible AI, model monitoring, and MLOps/LLMOps frameworks.