Senior Applied AI Engineer, AI Platform

Role overview

Join the AI Platform team as a Senior Applied AI Engineer focused on turning AI from a working feature into a trusted layer of fund operations infrastructure. The scope is end-to-end product engineering—architecture, evaluation, integration, and deployment—rather than pure research. You will build agents that automate document-heavy workflows in private markets while establishing the patterns other engineering teams adopt.

Responsibilities

- Design, build, and ship agents that automate real fund-operations workflows such as subscription document parsing, capital call chasing, and portfolio data reconciliation, owning them from prototype through production and beyond - Build and improve the evaluation layer, including test cases derived from real documents, regression suites in CI, human review where correctness is non-negotiable, and measurable movement on accuracy, latency, and cost - Own the AI application architecture: orchestration, multi-agent design, tool contracts, memory, and context engineering (RAG, MCP) with clear domain boundaries, guardrails, and human-in-the-loop controls - Run agent workloads in production with versioned rollouts, feature flags, rate limits, multi-provider fallback, regional failover under EU data residency, and observability that turns traces into fixes - Consolidate document parsing and extraction under the platform team, and turn shared evaluation and observability into something other engineering teams consume rather than rebuild - Set engineering patterns, partner closely with developer experience, and mentor engineers across teams on agent and LLM practice

Requirements

- 5+ years building production software, including at least one agent or LLM-powered capability taken live and still owned in production - Deep experience with agent orchestration and context engineering—tool contracts, memory, RAG, multi-agent design—using frameworks such as Mastra, Vercel AI SDK, LangGraph, or similar - Track record of integrating agents into a real product with its authorization model, not standalone prototypes handed off to others - Ability to make non-deterministic systems measurable through test cases on real data, regression suites, and human review, and to tell genuine improvement from benchmark drift - Production ownership skills: reading traces, diagnosing failure modes, tuning cost and latency, handling provider rate limits and fallback without drama - Backend experience with TypeScript and/or Python

Nice to have

- Experience in fintech, private markets, or another regulated, document-heavy domain - Familiarity working under EU data-residency constraints - A platform mindset that builds for other engineers and earns adoption through trust, not just written standards

Benefits and work setup

- Customizable benefits package covering wellbeing, sport, mobility, food, and more - 28 days of vacation plus 2 company days and local public holidays - Remote setup with up to 6 remote calendar weeks per year - Modern tech and work setup provided - Diverse team of 130+ people from 40+ countries