• Design and build production-ready AI/ML systems, with an emphasis on Standard Model LLM-powered product and platform features. • Leverage LLM tooling/APIs such as LangChain and MCP connectors to implement retrieval-augmented generation (RAG), copilot-assistants and agentic workflows. • Partner with Product Management and product teams to translate requirements into AI-powered capabilities that surface directly in user-facing products. • Apply MLOps and LLMOps best practices: monitoring, evaluation, prompt versioning, cost/performance optimization. • Combine traditional AI/ML with modern GenAI approaches to deliver hybrid solutions where appropriate. • Collaborate with Data Engineers to establish a scalable data pipeline that serves structured data shaped for LLM consumption, feature store data for traditional AI, and Trad/GenAI-enhanced insights for internal and customer-facing BI use cases. • Mentor and upskill peers in on core AI/ML and LLMOps practices, raising the overall AI/ML competency of the team. • Stay current with developments in GenAI, LLMOps, generative AI safety frameworks, and evaluate their potential for adoption within the platform.