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Strong brand, metro location, and broad LLM/ML requirements increase candidate competition.
Core LLM/ML skills transfer across industries, but regulated finance experience increases domain specificity.
Multiple explicit years plus specialized LLM, MLOps, cloud, and graph requirements enforce strict shortlisting.
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Lead end-to-end development and operationalization of AI/ML and LLM-based solutions addressing complex enterprise-scale business problems.
Architect and implement advanced generative AI applications including prompt engineering, fine-tuning, embeddings, vector search, and retrieval-augmented generation (RAG).
Ensure compliance with Responsible AI, model governance, and regulatory requirements while collaborating with cross-functional teams and mentoring data scientists.
Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, Statistics, or related quantitative field.
8+ years overall IT experience; 5+ years in machine learning/statistical modeling and AI solution development; 2+ years specifically in generative AI/LLMs.
Proficiency in Python and major ML/AI frameworks (Scikit-learn, PyTorch, TensorFlow, Keras).
Experience with LLMs, NLP, prompt engineering, embeddings, vector DBs, Azure and/or AWS ML deployment, and graph databases (e.g., Neo4j).
Experienced in driving AI/ML projects from design to production at enterprise scale, with strong operational and stakeholder management skills.
Deep technical expertise bridging advanced analytics, generative AI, and AI platform engineering in regulated or complex enterprise environments.
Collaborates effectively across business, engineering, architecture, and governance teams, mentoring others and advancing AI best practices including Responsible AI.