Backend engineer in private equity

Most AI tooling for financial services is shallow RAG over pitch decks. This Series A startup is building the agentic operating system used by multi-billion-dollar private equity funds to source, underwrite, and monitor investments, scaling ARR 15x over the past twelve months. Backed by top-tier venture investors with $20M in total funding and teams in London and New York, they have achieved rare product-market fit in one of the most demanding, accuracy-critical domains in enterprise software.

• Architect and scale a streaming-first graph execution framework that powers autonomous multi-step agent reasoning in production.
• Build sandboxed code execution environments that can be paused, inspected, and resumed mid-run for stateful programmatic tool calling.
• Scale multimodal ingestion and VLM-driven OCR pipelines feeding self-hosted search clusters across high-volume institutional filings and financial reports.
• Engineer verification engines and data models that provide deterministic, end-to-end citations for every AI-generated insight.
• Own core backend services and asynchronous messaging pipelines end-to-end using Python, FastAPI, Postgres, OpenSearch, and RabbitMQ or Kafka.

• 5+ years building and operating distributed backend systems or data platforms at scale within a venture-backed startup or top-tier tech company.
• Deep production expertise in Python, FastAPI, PostgreSQL, search clusters like OpenSearch, and streaming or message brokers such as Kafka or RabbitMQ.
• Proven experience building with LLMs and agents, with a clear grasp of what it takes to make them reliable, deterministic, and observable in production.
• Track record of taking complex services from initial architectural design through to high-scale deployment and day-two operations.

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