Applied AI engineer for fund operations

bunch is building the backbone of private markets. We are enabling next-gen fund operations with one integrated system that combines secure data infrastructure, AI-powered workflows and expert fund services. If you value ownership, growth through real responsibility, and working with a thoughtful, ambitious team, this role might be for you. Your Role As a Senior Applied AI Engineer on our AI Platform team, you take AI at bunch from working to relied upon.

• Design agents that automate real fund-operations workflows, and own them from prototype through production and after.
• Build the evaluation layer: test cases built from real documents with the output we expect, regression suites in CI, human review where correctness is non-negotiable, and clear success criteria for an agent completing a complex task end to end.
• Then move the numbers that matter — accuracy, latency, cost.
• Own the AI application architecture.
• Orchestration and multi-agent design, tool contracts, memory, and context engineering (RAG, MCP) with clear domain boundaries — plus the guardrails, approvals, and human-in-the-loop controls that anything touching investor money requires.
• Versioned, feature-flagged rollout of agent versions; rate limits, provider fallback, and regional failover within EU data residency; and the observability to trace a failure across services and turn it into a fix rather than a theory.
• Document parsing and extraction consolidates into this team, and shared evaluation and observability become something other teams consume rather than rebuild.
• You set the patterns other engineers inherit, partner closely with DevX, and mentor engineers across teams on agent and LLM practice.
• Review our MCP offering end to end and ship it to the first customers, with the access boundaries, evaluation and observability a customer-facing surface needs.
• Stand up shared observability for our AI workloads — token usage, estimated cost, latency, failures and retries per provider — on a dashboard people actually open during an incident.
• Publish v1 of our agent patterns (domain boundaries, tool contracts, prompt and eval conventions), reviewed with DevX and adopted by at least one team outside AI Platform.

• 5+ years building production software, including at least one agent or LLM-powered capability you took end to end and still owned once it was live
• real depth in orchestration and context engineering — tool contracts, memory, RAG, multi-agent design — with Mastra, ai-sdk, LangGraph or similar.
• You've integrated agents into a real product and its authorization model, not built prototypes for someone else to productionise
• you know how to make a non-deterministic system measurable, from test cases built on real data to regression suites and human review, and you can tell the difference between a model that improved and a benchmark that got easier
• you own what you ship. Reading traces, diagnosing failure modes, tuning cost and latency, handling provider rate limits and fallback without drama
• You have experience with TypeScript and/or Python.
• you build for other engineers as much as for end users, and the standards you set get adopted because people trust you, not because they are written down
• you start from the business outcome, choose the deterministic solution when it's the right one, and know when good enough is good enough
• Будет плюсом: Experience in fintech, private markets, or another regulated, document-heavy domain is a plus, as is working under EU data-residency constraints

• Customizable benefits package (wellbeing, sport, mobility, food, and more)
• 28 days of vacation, plus 2 company days and local public holidays
• Remote setup
• Up to 6 remote calendar weeks a year
• A great tech and work setup
• Work with a diverse team of 130+ bunchies from 40+ countries, with leaders who are best-in-class in their domains