Software Engineer - AI

Responsibilities

• 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: 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.

Requirements

• 2+ years of experience building production software, with strong proficiency in Python programming.
• 2+ years of experience working with and optimizing relational databases (e.g., SQL, PostgreSQL).
• Experience with cloud infrastructure (AWS or similar).
• 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.

Nice-to-Haves

• Experience designing scalable RAG pipelines for knowledge bases.
• Building conversation engines with memory, context, and state.
• Implementing LLM tooling and function-calling for system integration.
• Designing evaluation harnesses and datasets for AI feature quality.
• Preventing hallucinations and improving grounded response generation.

Tech Stack

• Backend: Python
• Data: PostgreSQL
• AI Systems: LLMs, embeddings, vector retrieval, RAG pipelines
• Infrastructure: AWS, Docker
• Developer Tools: GitHub, Linear, Claude Code, Cursor, CI/CD, automated testing

Benefits

• Competitive compensation and stock equity plan
• Comprehensive benefits package that includes medical, dental, vision, and life insurance
• Company sponsored pre-tax retirement savings program (401k)
• Flexible work environment that supports working from home
• Flexible PTO
• Parental Leave
• Home office stipend