Senior AI Engineer (Netherlands)

About the Role

We’re building AI into STARLIMS, a platform used across quality manufacturing, life sciences, public health, forensics, and environmental sciences.

This role is focused on agentic systems: software that reasons over a task, calls tools, works through multiple steps, and hands the result to a person to review and approve.

Our users work under strict accuracy, traceability, and validation requirements. The engineering challenge is making non-deterministic systems reliable, observable, and controllable enough to be trusted, tested, and shipped.

You’ll work on both the platform and runtime our agents execute on and the production agents built on top of it.

What You’ll Work On

Agent Platform & Runtime (Core Focus)

Design and build the runtime our agents execute on: planning and execution loops, tool calling, state management, durable execution, and failure recovery

Build the layer through which agents reach platform data and external systems safely

Design coordination, delegation, and handoff across agents and workflows where needed

Make agent behavior versionable, testable, measurable, and regression-safe across releases

Build reusable primitives so new agents are configured rather than rebuilt from scratch

Building Agents (Core Focus)

Take a domain workflow from expert conversation to a working agent: goals, actions, execution flow, failure handling, and success criteria

Ground agent decisions and outputs in authoritative enterprise data rather than relying on model knowledge alone

Implement human-in-the-loop by design, including approval gates, override capture, uncertainty handling, and clear evidence for agent decisions. Agents recommend and draft; people decide

Close the loop: turn user corrections and overrides into signals that measurably improve the agent

Evaluation & Reliability

Build evaluation harnesses for multi-step behavior, not single-response accuracy: task completion, tool-call correctness, groundedness, trajectory quality, and regression across model, prompt, and tool changes

Define production metrics for agent quality, reliability, latency, cost, and human intervention rates

Implement guardrails, fallbacks, timeouts, cost ceilings, and end-to-end observability and tracing across agent runs

Design safeguards against prompt injection, unsafe tool use, excessive permissions, data leakage, and other agent-specific security risks

Manage prompt evolution, model drift, and non-determinism while maintaining consistent, measurable system behavior across releases

Integration & Data

Integrate agents with platform APIs and third-party enterprise systems already running in our customers’ environments

Build retrieval and context pipelines that turn fragmented enterprise data into reliable, permission-aware agent context

Design controlled execution paths for automated actions, with a complete, traceable audit trail

Platform & Infrastructure

Build and operate backend services on AWS (Lambda, API Gateway, DynamoDB, Step Functions, etc.)

Own significant parts of the system architecture and contribute to key technical decisions

Contribute to infrastructure-as-code and deployment pipelines

Tech Stack

Languages: TypeScript, Python

Backend: Node.js, Python, AWS Lambda, Step Functions

AI: OpenAI, Anthropic, MCP and related agent/tool protocols, embeddings and vector search

Frontend: React, Next.js, Tailwind CSS

Infrastructure: AWS, Terraform

Testing: Jest, Playwright, pytest

Must Have

6+ years of software engineering experience, including production systems

Experience building production LLM systems, including tool-using or multi-step agentic workflows beyond simple prompting and chat interfaces

Strong understanding of LLM behavior, limitations, and failure modes, especially how errors compound across a multi-step run

Experience with LLM APIs, tool and function calling, and designing planning and execution loops

Experience evaluating and debugging non-deterministic systems

Solid backend and cloud experience (AWS or equivalent)

Proficiency in TypeScript and/or Python

You Should Be Comfortable With

Debugging across distributed and non-deterministic systems

Making explicit tradeoffs between accuracy, latency, reliability, and cost

Working in ambiguous problem spaces where the right architecture isn't obvious yet

Owning production systems end-to-end

Choosing conventional software over AI when AI isn't the right solution

Nice to Have

C#, Microsoft .NET Framework

Tool and interop protocols such as MCP

Evaluation pipelines and metrics built specifically for agentic systems

Experience in regulated or domain-heavy systems (validation, audit trails, controlled change)

Retrieval and grounding techniques for supplying agent context

Workflow and durable-execution platforms (Temporal, Step Functions, n8n, etc.)

Containerization and orchestration (ECS, EKS, Kubernetes)

Infrastructure as Code (Terraform or similar)

STARLIMS is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, creed, religion, color, national or ethnic origin, citizenship, sex, sexual orientation, gender identity and expression, genetic information, veteran status, age or disability status.  Originally posted on Himalayas