Staff Software Engineer, Core AI

Architect 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 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

Master 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 leadership and mentorship to the growing AI team, establishing best practices for AI product development, deployment, and governance

10+ years of professional software engineering experience with 4+ years focused on building backend for production applications 

Mastery of Python, Familiarity with some AI application frameworks, context engineering, and scalable system design for AI products

Expertise in 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 lead tech product initiatives, establish 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

 

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