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What You Will Work On ​​Build and evolve our Conversation Engine powering pre-drafted email, chat, and voice responses, including conversation state, memory, and high-quality response generation. Own the RAG pipeline end-to-end: document ingestion, chunking strategies, embeddings, indexing, retrieval (hybrid/vector), reranking, and grounded response generation. Implement AI Tooling / function calling to connect LLM workflows to internal systems (e.g., account lookup, case context retrieval, knowledge base queries) with strong validation and safe execution patterns. Design evaluation and quality systems for AI features: offline eval harnesses, golden datasets, human feedback loops, monitoring for hallucinations/grounding, and regression prevention. Collaborate with cross-functional teams to define, design, and ship new features. Work closely with business stakeholders and customers to translate requirements into technical specifications and documentation. Mentor and support engineering team members, promoting team efficiency and growth. Troubleshoot and debug complex issues, ensuring timely resolution and platform stability. Optimize application performance, reliability, and scalability, and uphold high standards for clean, maintainable code. Identify and proactively address technical debt and performance bottlenecks to drive iterative product improvement. What We’re Looking For 5+ years of professional experience in Python development. 5+ years of experience with relational databases (e.g., PostgreSQL). 2+ years of experience with cloud environments, particularly AWS. Proven experience working with Large Language Models (LLMs) and building production-ready RAG pipelines. Strong proficiency in API design, data modeling, relational database design, and testing methodologies. Proficiency with modern DevOps practices: version control (Git), containerization (Docker), CI/CD (GitHub Actions), and automated testing frameworks.