AI Engineer - Machine Learning Pipelines for NATO
WLG
The Hague, South Holland, Netherlands
Op locatie
Senior
7 dagen geleden geplaatst
Vacaturesamenvatting
Join a multinational defense organization to develop and maintain AI models and pipelines. You'll work with real datasets, ensuring reliability and traceability in a secure environment.
Vereiste vaardigheden
Kubernetes
Machine learning
Retrieval-Augmented Generation (RAG)
LangChain
Lifecycle Management
Backend Development
Testing
Security
Training
Writing
Cloud
Airflow
Monitoring
Containers
Observability
Operational Control
PYTHON
Techniques
Data Quality
Tools
Artificial Intelligence (AI)
service
SOLID
Databases
Docker
Next.js
NoSQL
Technology
TypeScript
Version Control (Git)
Production
Performance
Reliability
Software Engineering
Discipline
REST
Workflow orchestration
LlamaIndex
Packaging
Continuous Integration
Transformation
Data Science
Model Evaluation
Generative AI
Automated Processes
Helm
Traceability
Node.js
Programming
Applied AI
Embeddings
Measurement
Data Pipelines
Pydantic
Loading
Engineering Standards
Provisioning
Delivery
Fastapi
Front-end
Large Language Models (LLM)
MLOps
Vector
roadmaps
observing
Maintainability
Progress
A multinational defense organization is establishing its own applied AI capability and seeks an engineer to take models into service. In this role, you will work with real datasets and tackle real problems while owning the pipeline as much as the model itself. Reliability and traceability are paramount in this environment.
The Role
You will bring machine learning and data science methods to new datasets and questions, critically evaluating performance and data quality. Your responsibilities include:
•
Identifying and resolving issues in models, pipelines, and datasets, ensuring solutions are production-ready.
•
Designing, writing, testing, documenting, and refactoring programs, scripts, and AI components.
•
Adhering to the organization's engineering standards and secure development practices for maintainability.
•
Supporting the entire lifecycle: gathering requirements, team collaboration, and automating build, test, release, and monitoring processes.
•
Defining AI modules for integration, producing build definitions, and validating modules against functional, quality, security, and performance criteria.
•
Building and enhancing data pipelines, including extraction, transformation, and loading tasks.
•
Keeping colleagues informed about progress, risks, and blockers, and sharing delivery ownership through reviews.
•
Observing field implementations and contributing to technology assessments, roadmaps, and internal knowledge sharing.
Over WLG
WLG
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