Senior Software Engineer, Core AI

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

Nice To Haves

• 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