Senior AI Platform Engineer — Agent Automation

bunch; Viseu; Remote; EUR 70,000 - 105,000; Full time

Benefits offered by this job

Customizable benefits package

28 days vacation + 2 company days + PH

Remote setup up to 6 remote weeks/year

Job summary

bunch is seeking a Senior Applied AI Engineer to join the AI Platform team in taking AI from prototype to production-ready systems. You’ll own architecture, evaluation, integration, and deployment of multi-agent workflows and guardrails across EU-resident data and services, with measurable improvements in accuracy, latency, and cost.

You will design and ship agents that automate fund-operations workflows, integrate with our data model and authorization, build robust evaluation layers, and ensure

Qualifications

• 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.
• Agents: real depth in orchestration and context engineering — tool contracts, memory, RAG, multi-agent design — with Mastra, ai-sdk, Lang Graph or similar.
• You've integrated agents into a real product and its authorization model, not built prototypes for someone else to productionise
• Evaluation: 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
• Production: you own what you ship.
• Reading traces, diagnosing failure modes, tuning cost and latency, handling provider rate limits and fallback without drama
• Backend: You have experience with Type Script and/or Python.
• Platform mindset: 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
• Pragmatic: you start from the business outcome, choose the deterministic solution when it's the right one, and know when good enough is good enough
• Experience in fintech, private markets, or another regulated, document-heavy domain is a plus, as is working under EU data-residency constraints

Responsibilities

• Design agents that automate real fund-operations workflows, and own them from prototype through production and after.
• They integrate with our services, data model, and authorization system — they don't sit beside the product as standalone prototypes.
• Evaluate and improve agent performance.
• 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.
• Run it in production.
• 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.
• Make it a platform, not a project.
• 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 Dev X, and mentor engineers across teams on agent and LLM practice.
• Your First 90 Days 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 Dev X and adopted by at least one team outside AI Platform.

Skills

Agent orchestration

LLMs & AI workflows

Production software

TypeScript

Python

Observability

System design

Tools

Mastra

ai-sdk

Lang Graph

Kubernetes

Google Vertex

AWS Bedrock

Datadog

FusionAuth

Retool

Job description

bunch is seeking a Senior Applied AI Engineer to join the AI Platform team in taking AI from prototype to production-ready systems. You’ll own architecture, evaluation, integration, and deployment of multi-agent workflows and guardrails across EU-resident data and services, with measurable improvements in accuracy, latency, and cost.

You will design and ship agents that automate fund-operations workflows, integrate with our data model and authorization, build robust evaluation layers, and ensure