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Tier-1 brand plus specialized ML/LLM skills but senior requirement and niche expertise limit applicant density.
Highly domain-specific LLM, generative AI, graph and MLOps skills reduce cross-industry transferability.
Explicit 8+ years, 5+ years relevant ML, and specialized LLM/MLOps skills make filters stringent.
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Lead end-to-end design, development, deployment, and monitoring of AI/ML and LLM-based solutions addressing enterprise-scale business problems.
Architect and implement advanced generative AI applications including prompt engineering, fine-tuning, embeddings, vector search, and RAG.
Drive best practices in model evaluation, governance, Responsible AI compliance, and mentor data scientists while collaborating with cross-functional teams.
Bachelor’s, Master’s or higher in Data Science, Computer Science, Mathematics, Statistics, or related quantitative field.
8+ years IT experience with 5+ years in machine learning and AI solution development including 2+ years in generative AI/LLMs.
Proficiency in Python and ML frameworks (e.g., Scikit-learn, PyTorch, TensorFlow), experience with LLMs, prompt engineering, embeddings, vector DBs, and cloud AI/ML platforms (Azure/AWS).
Experience with model lifecycle management, Responsible AI practices, and production deployment at enterprise scale.
Demonstrated success leading AI/ML solutions in complex, high-impact enterprise settings with strong delivery ownership.
Technical depth in generative AI, large language models, model governance, and operational AI/ML workflows (MLOps/LLMOps).
Skilled at stakeholder collaboration across business, engineering, architecture, and governance domains with ability to communicate complex concepts to varied audiences.