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.