Backend Python/AI Engineer

Role SummaryWe are looking for a hands-on backend engineer to build the plumbing that connects AI models to BXMA's data and documents: Model Context Protocol (MCP) integrations, Snowflake plug-ins, and pipelines that process first-party research (investment memos, financial models and call notes) so it can be searched, summarized and used safely by AI tools.What You Will DoDesign, build and maintain MCP servers and integrations that expose BXMA data sources and internal applications to AI assistants and agentsDevelop Snowflake plug-ins and data access layers (SQL, Snowpark, Cortex or similar) with proper entitlements and governanceBuild document-processing pipelines for first-party research (memos, Excel models, call notes, PDFs, Word documents): parsing, chunking, metadata and entity tagging, embeddings and retrieval (RAG)Ship production-grade services: APIs, testing, CI/CD, monitoring, logging, and cost and performance tuningProvision and manage cloud infrastructure as code (e.g., Terraform on AWS)Partner with investment, risk and operations users to turn workflows into reliable AI tools, and evaluate output qualityDocument work thoroughly and support knowledge transfer to employees over timeMust-Have QualificationsBachelor's degree in Computer Science, Engineering, Mathematics or a related field, with 3–7 years of professional software engineering experienceStrong Python, with experience building backend services and REST/streaming APIsHands-on experience with LLM application development: tool/function calling, RAG, embeddings and vector search, and prompt and evaluation design (MCP experience strongly preferred)Solid SQL and data-engineering skills; Snowflake experience strongly preferredExperience extracting structured data from unstructured documents (PDF, Word, Excel)Cloud experience (AWS preferred), containers and Git-based CI/CD; Terraform or similar infrastructure-as-code toolingClear communicator who can work directly with non-technical business usersStrongly PreferredHedge fund, multi-asset or asset-management experience (e.g., familiarity with portfolio, risk, P&L, fund and manager research data, investment memos and models); this is a priority where possibleExperience in regulated financial-services environments, including data entitlements, security and auditabilityExposure to agent frameworks, LLM evaluation and observability tooling, or enterprise AI platforms (e.g., Microsoft 365 Copilot, Anthropic or OpenAI APIs)TypeScript/Node.js experienceWhat Success Looks Like (First Six Months)Production MCP integrations live for priority BXMA data sourcesSnowflake-backed AI data access in use by at least one investment or operations teamA repeatable pipeline processing first-party research documents, with measurable retrieval qualityCode, runbooks and documentation in place to support transition to the internal team