Lead AI use-case discovery, design, prototyping and delivery. Partner with business leaders to frame outcomes, design architectures (RAG, LLMs, cloud AI), build MVPs using Python/APIs and low-code tools, and drive adoption with governance and responsible AI practices.

Build and own end-to-end NLP and ML solutions (including LLMs, RAG, embeddings) to extract structured information from documents. Responsibilities include problem definition, data exploration, model development, evaluation, productionization, monitoring, and cross-team collaboration to improve data quality and coverage.

Design, build, and operationalize a unified AI/ML data collection platform. Responsibilities include LLM/RAG pipelines, data ingestion, feature management, model lifecycle and deployment, observability, scalable distributed systems, and mentoring engineers to deliver production-ready ML solutions.