LLM & MLOps Engineer for Secure AI Pipelines

WLG; Den Haag; On-site; EUR 90,000 - 130,000; Full time

Job summary

WLG is seeking an engineer to take a model from development to production, working on real datasets and problems. You will own the pipeline around the model, aligning with reliability and traceability standards in a defence context.

You will design, test and refactor AI components, ensure secure practices, and support the full lifecycle from data gathering to monitoring. Collaboration across teams and constant knowledge sharing are expected.

Qualifications

• Hands-on history of developing, optimising and deploying AI pipelines.
• Strong programming alongside ML; software engineering discipline.
• Experience with pre-trained/foundation models and LLMs.
• In-depth knowledge of model evaluation and lifecycle.
• Practical use of retrieval-augmented generation and vector stores.

Responsibilities

• Bringing ML and data science methods to new datasets and questions.
• Identify and fix issues in models, pipelines and data.
• Design, write, test and refactor AI components and scripts.
• Adhere to engineering standards and secure development practices.
• Support full lifecycle: gather needs, plan work, automate CI/CD.
• Define AI modules, build definitions, validate against criteria.
• Build and improve data pipelines: extraction, transformation, loading.
• Communicate progress, risks and blockers; share ownership via reviews.
• Monitor field arrivals and contribute to roadmaps and knowledge sharing.

Skills

ML in production

Python programming

Software engineering

CI/CD

MLOps

Tools

LangChain

LlamaIndex

Pydantic

FastAPI

Docker

Kubernetes

Airflow

Argo

Job description

WLG is seeking an engineer to take a model from development to production, working on real datasets and problems. You will own the pipeline around the model, aligning with reliability and traceability standards in a defence context.

You will design, test and refactor AI components, ensure secure practices, and support the full lifecycle from data gathering to monitoring. Collaboration across teams and constant knowledge sharing are expected.