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Develop tactical insights from large structured and unstructured datasets, build and validate ML models and AI tools, design scalable resilient distributed systems, collaborate with analytics and data teams, communicate results to technical and executive stakeholders, and influence departmental strategy while independently driving technical approaches.

Technical lead owning end-to-end decision intelligence: feature engineering on Databricks Lakehouse, optimization and sequential decision-making, scoring and deployment, experimentation, production monitoring, and technical team leadership and mentorship. Drive architecture, translate business objectives into reward/constraint formulations, and champion AI-assisted engineering.

Lead the product backlog and roadmap for an AI-powered Next Best Action platform. Coordinate data pipelines, model feedback, and channel integrations (Adobe AEP, email/SMS/direct mail). Write implementation-ready user stories, manage rules/eligibility governance, align stakeholders on model and campaign outcomes, and ensure data quality for ML training and scoring.

Design, build, and operate production-grade LLM-powered services and APIs. Own data and inference pipelines, reliability, performance, cost, and observability. Drive engineering best practices, CI/CD, testing, and collaborate with data scientists to deploy and continuously improve agentic and RAG-based AI systems at scale.