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# AI Engineer

- **Company:** Fitura
- **Location:** Utrecht, Netherlands
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Microsoft Azure, Software Code Optimization, Continuous Integration, Human-Computer Interaction, Information Retrieval, Python (Programming Language), Machine Learning, Language Modeling, Performance Tuning, Webui, Software Deployment, Software Engineering, Retrieval-Augmented Generation, Large Language Models, Generative AI, LangSmith, Information Technology, Production Code, RAGAS (Retrieval Augmented Generation Assessment), Machine Learning Operations, Data Pipelines, Databricks
- **Published:** October 28, 2026
- **Apply:** https://www.adzuna.nl/details/5943358006

## About the Role

* Education (Hard Requirement): Master's degree (WO) or equivalent academic level in Data Science, Artificial Intelligence, Computer Science, Mathematics, Statistics, or a related quantitative field.
 * Experience & Track Record (Hard Requirements):
 + Minimum of 3+ years of professional experience in machine learning, applied AI, or data science.
 + Demonstrable track record of improving production-grade RAG systems or advanced retrieval-based AI solutions.
 + Strong hands-on proficiency in Python, Databricks, LLM architecture, and Azure cloud environments.
 + Proven experience designing evaluation methodologies, quality metrics, and continuous feedback loops for GenAI systems.
 + Proficiency in written and spoken English (corporate business language).
 * Nice-to-Have Technical Experience:
 + Experience with fine-tuning language models and reranking algorithms.
 + Hands-on experience with AI observability tools (e.g., MLflow, TruLens, Ragas, LangSmith).
 + Knowledge of synthetic evaluation dataset creation and benchmark curation.

## Description

As an AI Engineer - RAG Platform Optimization within banks central AI capability, you take ownership of continuously improving the accuracy, retrieval quality, and response effectiveness of the bank's enterprise Retrieval-Augmented Generation (RAG) platform. Operating at the convergence of Information Retrieval, Data Science, and Software Engineering, your core mission is failure analysis, hypothesis-driven experimentation, and evaluation framework optimization. Rather than managing people or building standard web UI applications, you focus strictly on measurable model quality-analyzing production telemetry in Databricks, running chunking/embedding experiments, building continuous feedback loops, and ensuring every release raises the performance benchmark for thousands of internal users., * Continuous RAG Model Optimization: Perform failure analysis on low-quality responses, identify underlying retrieval or grounding flaws, and run large-scale experiments on chunking, embedding models, prompt optimization, and reranking strategies.

 * GenAI Evaluation & Measurement Frameworks: Build and enhance automated evaluation pipelines to measure retrieval precision/recall, response correctness, hallucination rates, and user satisfaction metrics.
 * Data Analysis & Telemetry Insights: Analyze user interaction data, production telemetry, and evaluation datasets within Databricks to detect performance regressions and uncover quality improvement opportunities.
 * Production Pipeline Implementation: Translate data-driven experimentation results into scalable, maintainable Python production code and automated CI/CD data pipelines on Azure.
 * Cross-Functional Technical Alignment: Partner with Software Engineers, Data Scientists, Platform Engineers, and Product Managers to embed evaluation metrics into automated release pipelines.

Your Deliverables:

 * Automated GenAI evaluation suites and synthetic benchmark datasets tracking retrieval and response quality.
 * Optimized chunking, embedding, reranking, and prompt strategies deployed directly into production pipelines.
 * Production telemetry dashboards in Databricks tracking model performance metrics, error categories, and user trust indicators.
 * CI/CD-integrated testing scripts that prevent quality regressions prior to production deployments.