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# Data Scientist & LLM Engineer

- **Company:** UUG AI
- **Location:** Gent, Belgium
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Computer Vision, Python (Programming Language), Language Modeling, MongoDB, Search Technologies, Unstructured Data, Management of Software Versions, Retrieval-Augmented Generation, Large Language Models, Machine Learning Operations
- **Published:** October 29, 2026
- **Apply:** https://www.vdab.be/vindeenjob/vacatures/74927925

## About the Role

Strong Python skills and the ability to analyse data and build production software. Practical experience with LLMs, vision-language models, retrieval or adjacent applied-ML systems. Ability to evaluate probabilistic systems beyond hand-picked examples. Comfortable with structured and unstructured data, provenance, access and data quality. Clear communication of trade-offs and a preference for simple, testable approaches.

Helpful, not all required: RAG, tool use, agent harnesses and structured output; multimodal text/image/video models; MongoDB aggregation, vector search or large event datasets; evaluation tooling, observability and prompt versioning; media pipelines, computer vision or time-series analytics. Relevant skills and practical experience matter; no mandatory diploma or fixed number of years.

## Description

UUG.AI turns camera streams into useful, real-time signals for operations teams. As a Data Scientist & LLM Engineer, you turn recordings, detections, tracks, events and operational context into dependable product features. Build evaluated, observable language and vision-language workflows that help users understand what happened.

Your responsibilities:

 - Build vision-language and LLM workflows for summarisation, event understanding, search and operational automation.

 - Design evaluations, test sets and feedback loops measuring quality, safety, latency and cost.

 - Explore event and media metadata to identify patterns that can become product capabilities.

 - Develop retrieval and context strategies across structured data, model results and selected media evidence.

 - Productionise experiments as tested services or workflow stages, with monitoring and clear failure behaviour.

 - Collaborate with product and engineering colleagues to scope problems and iterate from feedback.