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Tier-1 brand and Bangalore location increase competition, but senior specialized LLM skills moderate density.
Strong ML/AI specialization combined with regulatory and model-governance expectations raises industry-specific fit sensitivity.
Explicit multi-level years requirements plus mandatory LLM/MLOps and governance skills create strict filters.
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Lead end-to-end delivery of AI/ML and LLM-based solutions including design, development, deployment, and monitoring at enterprise scale.
Architect and implement generative AI applications involving prompt engineering, fine-tuning, embeddings, vector search, and RAG techniques.
Drive model governance, Responsible AI compliance, build reusable AI assets, and mentor data scientists across teams.
8+ years of IT experience and 5+ years in machine learning/statistical modeling/AI solution development with at least 2 years in generative AI/LLMs.
Bachelor’s or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or related quantitative field.
Strong proficiency in Python and ML frameworks like Scikit-learn, PyTorch, TensorFlow, or Keras.
Hands-on experience with LLMs, NLP, prompt engineering, embeddings, vector databases, RAG, Azure or AWS AI/ML deployment, and graph databases (e.g., Neo4j).
Experienced in translating complex business needs into scalable, secure AI/ML solutions in large enterprises with cross-functional collaboration.
Skilled in advanced generative AI technologies, model lifecycle management, Responsible AI practices, and model governance.
Demonstrated leadership in mentoring data scientists and fostering best practices within AI/ML teams.