Role purpose
You will design, build, and run cloud-native platform services that support data science, engineering, and AI/ML teams. The role sits between Cloud Engineering, DevOps, and MLOps, with a strong focus on AWS, automation, and scalable AI platforms.
Main responsibilities
Manage and design AWS infrastructure (Lambda, S3, Kinesis, API Gateway, containers, networking, landing zones).
Build Infrastructure as Code using Terraform.
Create and operate platforms for ML/AI: model training, serving, versioning, monitoring, Jupyter environments, APIs for inference, and GenAI/LLM use cases.
Automate data pipelines using Airflow, Spark, and Python.
Build and improve CI/CD with GitHub Actions, Jenkins, or AWS tools.
Handle incidents, root-cause analysis, user support, and on-call when needed.
Collaborate internationally and continuously improve systems and documentation.
Required profile
Strong AWS experience.
Python, data/ML ecosystem knowledge.
Docker, Kubernetes, Terraform.
API-driven architectures.
Experience with Airflow, Spark, GitHub/Bitbucket, AWS CI/CD tools, Lambda, S3, Kinesis.
Understanding of Generative AI and LLMs.
Nice to have
MLflow or model management tools.
AWS SageMaker.
Basic Dutch.