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# Data Scientist/AI Engineer

- **Company:** Just Eat Takeaway.com
- **Location:** Amsterdam, Netherlands (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Data Analysis, Unit Testing, Code Review, Continuous Integration, Data Visualization, Software Debugging, Monitoring of Systems, Python (Programming Language), Operational Data Store, Regression Testing, Software Engineering, SQL Databases, Datadog, Data Logging, Large Language Models, Multi-Agent Systems, Generative AI, Backend, Agentic-AI, Git, Kubernetes, Low Latency, Data Analytics, Code Inspection, Observability for Autonomous Agents, Software Version Control, Docker
- **Published:** November 13, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=5f6af8c092a57647

## About the Role

* Hands-on experience building and shipping user-facing AI agents in production - LLM-based systems that real users interact with, where reliability, latency, safety and graceful handover to humans matter.
 * Practical experience with agent evaluation: building evaluation datasets, defining metrics, using LLM-as-a-judge, regression testing agents, and running offline and online experiments to compare prompts, models or agent designs.
 * Practical experience with agent observability and monitoring: tracing and debugging multi-step agent runs, defining quality/cost/latency signals, and building dashboards and alerts for LLM systems in production (e.g. OpenTelemetry, Datadog, or similar tooling).
 * Experience with agent frameworks (e.g. Google ADK, LangGraph, or similar), tool use / MCP, and multi-step or multi-agent designs.
 * Strong Python and SQL; good software engineering habits - typing, linting, unit tests, Git, CI/CD, code review.
 * A solid data science foundation: statistics, experiment design, and the ability to reason about whether an observed change is a real improvement.
 * Comfortable working independently on well-defined problems, knowing when to ask for guidance, and communicating results clearly to technical and non-technical stakeholders.
 * Nice to have: experience with feature flags and staged rollouts, containerised deployments (Docker/Kubernetes).

## Description

As a Data Scientist - AI Engineering, you will help build and run the AI agents that support the people who keep our delivery network moving. These are production, user-facing agents that help users resolve issues and take actions in real time, across multiple markets and languages.

Your mission is to make these agents measurably better and safe to change. You will build, evaluate, and monitor LLM-powered agents end to end: from designing agent behaviour and tools, to building the evaluation harness that tells us whether a change is an improvement, to the observability that tells us how the agent behaves in production. You will work closely with Product, Backend Engineers, Operations colleagues, and the other Data Scientists on the team.

These are some of the key components to the position:

*
Agent Development: Build and improve components of our agent systems - prompts and workflows, tool use, and handover to humans. Translate operational requirements into agent behaviour that is reliable and testable.

 * Agent Evaluation: Build and maintain evaluation suites for agents: datasets built from real conversations and scenarios, LLM-as-a-judge and deterministic metrics, and end-to-end tests of tool-using agents. Run offline experiments to compare models, prompts, and agent designs, and use the results to make clear ship / don't-ship recommendations. Help move us towards automated and well-grounded online evaluation.
 * Observability & Monitoring: Instrument agents with tracing, structured logging, and quality metrics. Build and maintain dashboards and alerts covering quality, latency, cost, and failure modes. Analyse production traces and historical conversations to find regressions and the next best improvement.
 * Production Standards: Ship agent changes safely - well-tested Python, code review, version control. Be mindful of production rules, data privacy and security.
 * Data Analysis: Use SQL and Python to analyse large volumes of conversation and operational data, define and track quality and business metrics, and quantify the impact of agent changes.
 * Actionable Insights: Translate evaluation and monitoring results into clear recommendations for the team and stakeholders, using data visualisation where it helps.
 * Cross-functional Collaboration: Work with Backend Engineers, Operations and Product to expand what our agents can do, and contribute to a growing internal community of people building user-facing agents at JET.
 * Process Improvement: Spot opportunities to automate evaluation, testing and release workflows, and make contributions to them.