Applied AI Engineer: Build & Deploy Production AI Pipelines

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

Job summary

WLG in The Hague is seeking an engineer to take a model all the way into service, working with real datasets and production-grade pipelines where reliability and traceability matter more than novelty.

You will own the full lifecycle from data extraction to deployment, fix issues in production, and design, test and refactor AI components while aligning with secure development practices and governance.

Qualifications

• Hands-on history developing and maintaining AI pipelines and deployable models.
• Strong software engineering practices applied to AI work.
• Solid understanding of model evaluation and improvement.
• Experience with pre-trained / foundation models and gen AI techniques.
• Familiarity with retrieval-augmented generation and vector stores.

Responsibilities

• Bringing ML methods to new datasets and assessing data quality and performance.
• Diagnosing and fixing issues in models, pipelines and datasets for production.
• Designing, testing, documenting and refactoring AI components.
• Working to secure development practices and maintainability.
• Automating build, test, release and monitoring across lifecycle.
• Defining AI modules, build definitions and validation against criteria.
• Building and improving data pipelines (ELT/ETL) feeding the system.
• Keeping stakeholders informed with progress and risks through reviews.
• Contributing to technology roadmaps and internal knowledge sharing.

Skills

AI pipelines

Software engineering

Model evaluation

Pre-trained models

LangChain / LlamaIndex

MLOps

REST APIs

Docker

Kubernetes

Observability

Databases

TypeScript / Node.js

Tools

LangChain

LlamaIndex

Pydantic AI

Docker

Kubernetes

Airflow

Argo

Job description

WLG in The Hague is seeking an engineer to take a model all the way into service, working with real datasets and production-grade pipelines where reliability and traceability matter more than novelty.

You will own the full lifecycle from data extraction to deployment, fix issues in production, and design, test and refactor AI components while aligning with secure development practices and governance.