What You'll Do

Contribute to the architecture and lead development of production AI products including intelligent chatbots, document processing systems, and agentic workflows using Python and modern AI frameworks
Design and implement components of our centralized AI platform including model routing, provider management, vector search, and AI application frameworks with seamless MCP (Model Context Protocol) integrations
Build scalable AI products that integrate with diverse technologies including accounting systems, document repositories, and external APIs while maintaining robust monitoring and observability
Implement context engineering and system design for AI applications, ensuring optimal information retrieval, context assembly, and multi-turn conversation management
Collaborate with Product, Engineering, and Security teams to ensure AI products are robust, compliant, and aligned with business objectives in the regulated accounting space
Provide technical guidance and mentorship to other engineers on the growing AI team, promoting best practices for AI product development, deployment, and governance

What You'll Bring

7+ years of professional software engineering experience with 3+ years focused on building backend for production applications
Strong proficiency in Python, alongside familiarity with AI application frameworks, context engineering, and scalable system design for AI products
Experience designing products that integrate with multiple technologies, APIs, and data sources in cloud-native environments (AWS preferred)
Strong desire to develop deep hands-on experience with LLM APIs, retrieval-augmented generation (RAG), conversational AI, document processing, and MCP integrations
Proven ability to own tech product initiatives, drive technical standards, and communicate complex system designs to both technical and business stakeholders

Experience building chatbots or conversational AI products in production
Background in system integration, API design, or enterprise software platforms
Familiarity with accounting workflows and financial data processing
Experience with AI observability, debugging tools, and production AI monitoring
Hands-on experience with advanced RAG architectures, reranking systems, and retrieval optimization techniques
Knowledge of reinforcement learning from human feedback (RLHF) or other RL techniques for improving AI product performance
Experience building AI platform components like embedding pipelines, semantic search systems, or multi-modal processing frameworks