Requirement: Machine Learning Engineer
Location: The Hague, NETHERLANDS
Full Time On-Site: Yes
Time On-Site: 100%
Not to Exceed Rate: 63 EUR
Total Scope of the request (hours): 836
Required Start Date: 13 July 2026
End Contract Date: 31 December 2026
Required Security Clearance: NATO SECRET
Please do NOT apply for any NATO contract positions unless you meet ALL the following criteria:
- Current National or NATO SECRET clearance
- Nationality of one of the NATO member countries
- Current work visa for the specific location if applying for an in-country position
Duties & Role:
- Apply established machine learning and AI techniques to new problems and datasets.
- Build, optimize, and maintain machine learning and AI models and supporting pipelines.
- Evaluate and monitor ML/AI system outcomes, model performance, and data quality; define appropriate metrics and acceptance criteria.
- Identify issues in models, pipelines, and datasets; recommend and implement improvements.
- Design, develop, test, document, refactor, and maintain moderately complex programs/scripts to support ML development and deployment.
- Follow agreed engineering standards, tools, and best practices to deliver secure, reliable, and maintainable solutions.
- Monitor progress, report status, and communicate risks, blockers, and dependencies in a timely manner.
- Collaborate with teammates through code reviews, design reviews, and shared ownership of deliverables.
- Elicit requirements for ML/AI lifecycle practices, working methods, and automation (e.g., CI/CD, testing, deployment, monitoring).
- Select and implement appropriate lifecycle practices for components and microservices within the ML/AI solution.
- Deploy automation to support well-engineered, repeatable, and secure build/release processes.
- Define ML/AI modules needed for integration builds and produce buIld definitions for each release/generation of the solution.
- Validate and accept completed ML/AI modules against agreed functional, quality, and performance criteria.
- Apply data science techniques to new problems and datasets, using specialized programming approaches where needed.
- Identify and implement opportunities to improve training data, features, and model performance.
- Build and maintain data pipelines using data engineering standards and tools (ETL/ELT).
- Support monitoring of emerging technologies and contribute to internal reports, technology roadmaps, and knowledge sharing.
Skill, Knowledge & Experience:
- The candidate must have a currently active NATO SECRET security clearance
- 5+ years of hands-on experience building ML/AI solutions in Python, with strong foundations in machine learning concepts, software engineering, and production-grade development practices.
- Proven experience designing, developing, optimizing, and maintaining end-to-end AI/ML pipelines (data processing, training, evaluation, deployment, and monitoring).
- Strong track record in model evaluation and performance measurement, including defining metrics, running assessments, and monitoring model qualitY over time.
- Experience applying and adapting pre-trained models (including Generative AI/LLMs) to solve specific business use cases.
- Solid experience with MLOps practices: version control, experiment tracking, model packaging, deployment, monitoring, and automation.
- Proficiency with CI/CD pipelines and DevOps best practices (e.g., Git-based workflows, build/release automation).
- Practical experience with containerization (Docker, Podman) and orchestration using Kubernetes, including infrastructure provisioning and operationalization in cloud environments.
- Experience with workflow orchestration tools such as Apache Airflow and/or Argo Workflows.
- Strong experience building and maintaining REST APIs, ideally for serving ML models and AI services.
- Experience working with SQL and NoSQL databases.
- Experience building production-grade AI agent backends, e.g., using LangChain or pydantic-ai, wrapped in FastAPI services.
- Full-stack experience with TypeScript frameworks such as Next.js.
- Experience working in air-gapped / restricted-network environments.
