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bunch is building the operating infrastructure for private markets, combining AI-powered automation with regulatory expertise to replace fragmented processes with an integrated platform. The company has 4x'd ARR in 2025, crossed 150 fund managers and 12,000 LPs, and closed a $35M Series B in May 2026.
As a Senior Applied AI Engineer on the AI Platform team, you will take AI from working to relied upon. bunch already runs AI in production—a document extraction pipeline live in fund operations, agent workflows on Mastra, evaluations, and multi-provider fallback inside EU data residency. You will build on this foundation to ship more agents that automate fund-operations workflows, establish evaluation frameworks that prove agents can be trusted with critical tasks, and create a platform that enables other teams to ship AI capabilities consistently.
Key responsibilities:
- Build and ship agents that automate real fund-operations workflows, owning them from prototype through production. Agents integrate with the company's services, data model, and authorization system.
- Evaluate and improve agent performance by building test cases from real documents, regression suites in CI, human review processes, and clear success criteria. Move metrics that matter: accuracy, latency, and cost.
- Own the AI application architecture: orchestration, multi-agent design, tool contracts, memory, context engineering (RAG, MCP), and guardrails/approvals/human-in-the-loop controls for systems touching investor money.
- Run it in production: versioned, feature-flagged rollouts; rate limits and provider fallback within EU data residency; observability to trace failures across services.
- Make it a platform: consolidate document parsing and extraction into this team, make shared evaluation and observability consumable by other teams, set patterns other engineers inherit, and mentor across the organization on agent and LLM practice.
First 90 days: Review and ship the MCP offering to first customers with proper access boundaries and evaluation; stand up shared observability for AI workloads (token usage, cost, latency, failures); publish v1 of agent patterns adopted by at least one team outside AI Platform.
Tech stack: TypeScript, Svelte, React (frontend); Node.js, Nest.js (backend); MySQL, PostgreSQL (database); Mastra, Vercel ai-sdk, Google Vertex (Gemini) with AWS Bedrock fallback (AI); Kubernetes on AWS (infrastructure); Datadog (observability); FusionAuth, Retool (auth/tools).
Benefits include customizable benefits package (wellbeing, sport, mobility, food), 28 days vacation plus 2 company days, remote setup with up to 6 remote calendar weeks per year, and a diverse team of 130+ from 40+ countries.
REQUIREMENTS:
- 5+ years building production software, including at least one agent or LLM-powered capability owned end-to-end in production
- Real depth in orchestration and context engineering (tool contracts, memory, RAG, multi-agent design) with Mastra, ai-sdk, LangGraph, or similar; integrated agents into real products with authorization models, not prototypes
- Ability to make non-deterministic systems measurable: test cases from real data, regression suites, human review; can distinguish between model improvement and benchmark drift
- Production ownership: reading traces, diagnosing failures, tuning cost/latency, handling provider rate limits and fallback
- Experience with TypeScript and/or Python
- Platform mindset: build for other engineers; standards adopted through trust, not documentation
- Pragmatic approach: start from business outcomes, choose deterministic solutions when appropriate, know when good enough is sufficient
- Plus: fintech, private markets, or regulated document-heavy domain experience; experience with EU data-residency constraints
