Backend Python/AI Engineer
Job Description
Role Summary
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We 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 Do
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- Design, build and maintain MCP servers and integrations that expose BXMA data sources and internal applications to AI assistants and agents
- Develop Snowflake plug-ins and data access layers (SQL, Snowpark, Cortex or similar) with proper entitlements and governance
- Build 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 tuning
- Provision 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 quality
- Document work thoroughly and support knowledge transfer to employees over time
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Must-Have Qualifications
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- Bachelor's degree in Computer Science, Engineering, Mathematics or a related field, with 3–7 years of professional software engineering experience
- Strong Python, with experience building backend services and REST/streaming APIs
- Hands-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 preferred
- Experience 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 tooling
- Clear communicator who can work directly with non-technical business users
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Strongly Preferred
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- Hedge 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 possible
- Experience in regulated financial-services environments, including data entitlements, security and auditability
- Exposure to agent frameworks, LLM evaluation and observability tooling, or enterprise AI platforms (e.g., Microsoft 365 Copilot, Anthropic or OpenAI APIs)
- TypeScript/Node.js experience
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What Success Looks Like (First Six Months)
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- Production MCP integrations live for priority BXMA data sources
- Snowflake-backed AI data access in use by at least one investment or operations team
- A repeatable pipeline processing first-party research documents, with measurable retrieval quality
- Code, runbooks and documentation in place to support transition to the internal team
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