C004854 Machine Learning Engineer (NS) - THU 4 Jun, The Hague

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

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

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