• Lead the end-to-end design, development, and deployment of machine learning models and GenAI solutions for manufacturing intelligence, yield optimization, supply chain analytics, and enterprise data platforms.
• Architect and implement data pipelines, feature stores, and model serving infrastructure on Lakehouse and cloud-native architectures (Databricks, Azure).
• Develop prompt engineering strategies and feedback loops for LLM optimization; capture and normalize LLM interactions into reusable Knowledge Artifacts.
• Implement Retrieval-Augmented Generation (RAG) pipelines, including document parsing, chunking, vectorization, and semantic search using embeddings and vector databases. Support domain-specific ontologies, taxonomies governance and versioning.
• Design agentic workflows using industry-standard frameworks for autonomous task orchestration and multi-step reasoning, with safe and controlled execution via constraints, policies, and fallback paths.
• Collaborate with cross-functional teams to define KPIs, success metrics, and business impact measurements for AI/ML initiatives.
• Mentor intermediate and junior engineers; conduct code reviews and establish best practices for ML engineering.
• Present technical findings and strategic recommendations to senior leadership, distilling complex results into actionable business insights.