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Large financial brand and Bengaluru location increase candidate density, but LLM specialization narrows the pool.
Core LLM/ML skills transfer across industries, but enterprise governance and financial domain needs increase bias.
Explicit 8+ years, mandatory ML/LLM, MLOps, and governance requirements produce strict shortlisting filters.
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Lead end-to-end design, development, evaluation, deployment, and monitoring of AI/ML, LLM, and generative AI solutions addressing complex, high-impact business problems at enterprise scale.
Architect and implement advanced generative AI applications including prompt engineering, fine-tuning, embeddings, vector search, and retrieval-augmented generation.
Drive best practices in model governance, Responsible AI compliance, reusable AI assets, MLOps / LLMOps pipelines, and mentor data scientists.
Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, Statistics, or related quantitative field.
8+ years overall IT experience with 5+ years in machine learning/statistical modeling/AI solution development; at least 2 years focused on generative AI/LLMs.
Strong proficiency in Python and ML frameworks (Scikit-learn, PyTorch, TensorFlow, Keras).
Experience with LLMs, NLP, prompt engineering, embeddings, vector databases, and RAG; proficiency with Azure and/or AWS for AI/ML deployment.
Proven track record in designing and operationalizing enterprise-scale AI/ML models, especially in generative AI and LLM domains.
Experienced in collaborating across business, engineering, architecture, and governance teams, demonstrating strong stakeholder management.
Familiarity with model governance, Responsible AI, MLOps / LLMOps, and mentoring or leadership in data science roles.