About
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.
Responsibilities
•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.
Requirements
•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.
Conditions
No conditions stated in the posting.
