# C004854 Machine Learning Engineer (NS) - THU 4 Jun

**Company:** [EMW, Inc.](http://jobs.workable.com/companies/rxTcVwBJTwXzUuxD5bzFUW.md)
**Location:** The Hague, Netherlands
**Workplace:** on site
**Employment type:** Contract
**Department:** AAS

[Apply for this job](http://jobs.workable.com/view/61de36c4-3a8f-411d-a3ee-09cccc932a5a)

## Description

**Deadline Date:** Thursday 4 June 2026

**Requirement:** Machine Learning Engineer

**Location:** The Hague, NL

**Full Time On-Site:** Yes

**Time On-Site:** 100%

**Total Scope of the request (hours):** 836

**Required Start Date:** 13 July 2026

**End Contract Date:** 31 December 2026

**Required Security Clearance:** NATO SECRET

**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.

## Requirements

**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.

Desirable:

-   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.