Machine Learning Engineer for NATO with security clearance

WLG

Job Location:

The Hague - Netherlands

Monthly Salary: Not Disclosed

Posted on: 20 days ago

Vacancies: 1 Vacancy

Job Summary

Would you like to join the leading international intergovernmental organization

The Machine Learning Engineer is responsible for the end-to-end development deployment and maintenance of machine learning (ML) and artificial intelligence (AI) solutions. This role requires a strong blend of data science software engineering and MLOps expertise to build robust scalable and secure AI/ML systems that address complex business challenges.

Responsibilities:

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.

Essential Qualifications & Experience:

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.

Nice to have

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 .
Experience working in air-gapped / restricted-network environments.

If youve read the description and feel this role is a great match wed love to hear from you! Click Apply for this job to be directed to a brief questionnaire. It should only take a few moments to complete and well be in touch promptly if your experience aligns with our needs.

Would you like to join the leading international intergovernmental organizationThe Machine Learning Engineer is responsible for the end-to-end development deployment and maintenance of machine learning (ML) and artificial intelligence (AI) solutions. This role requires a strong blend of data science...

Responsibilities:

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.

Essential Qualifications & Experience:

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.

Nice to have

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 .
Experience working in air-gapped / restricted-network environments.