AI engineer in private equity

Most AI tools in finance are brittle prompt wrappers over generic models. This Series A startup builds native agentic infrastructure for multi-billion-dollar private equity funds, automating technical sourcing, diligence, and portfolio analysis with fully cited audit trails. Backed by top fintech and venture investors and growing revenue more than ten-fold over the past year, they have achieved clear product-market fit with top-tier buyout and growth equity deal teams across the US and Europe.

• Design, ship, and scale autonomous agents that research companies, digest complex financial documents, and generate verifiable, cited analysis.
• Build proprietary foundation infrastructure instead of relying on generic wrappers, including a streaming-first graph execution framework and an interruptible, resumable code execution environment for tool use.
• Develop multimodal retrieval pipelines combining vision-language models for OCR, deep document search, and end-to-end claim attribution.
• Engineer production eval suites and feedback loops that serve as the definitive gate for whether harness modifications and model updates actually improve output quality.
• Collaborate closely with founders and private equity domain experts to turn complex, high-stakes diligence workflows into reliable programmatic systems.

• 5+ years building and scaling production backend services at a venture-backed startup or top-tier tech company, with full ownership of AI-powered systems.
• Proven track record taking LLM features, agents, or complex retrieval systems to production, with eval results and operational war stories to prove it.
• Deep expertise in Python and production search/data infrastructure such as OpenSearch, PostgreSQL, FastAPI, and distributed message queues.
• An eval-driven mindset that treats benchmarking, grounded verification, and error analysis as core engineering responsibilities, not an afterthought.

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