Data & ML Infrastructure Engineer

Optics11; Netherlands; On-site; EUR 90,000 - 130,000; Full time

Job summary

Optics11 in the Netherlands is expanding its team with a Data & ML Infrastructure Engineer who will own the platform for data ingestion, processing, storage, and ML workflows across products. You will design, implement, and improve a cloud-agnostic data/ML platform that supports multiple teams while ensuring reliability, scalability, and cost-efficiency.

You will collaborate with data scientists and IT to deliver robust infrastructure, automate deployments, and maintain governance, security, and

Qualifications

• MSc or equivalent with 5+ years in CS, data engineering, ML, or related field.
• Strong experience with data platforms, cloud infra, and distributed systems.
• Proficient with Infrastructure-as-Code (Terraform) and automated deployments.
• Experience with ML platforms/tools (MLflow, Feast, DVC, DataHub).
• Experience with AWS Sagemaker or Google Vertex AI.
• Experience with databases (MongoDB, PostgreSQL, InfluxDB).
• Experience with message brokers and streaming (Kafka, RabbitMQ).
• Hands-on with Docker/Kubernetes and orchestration.
• Experience with CI/CD (GitLab CI).
• Programming in Python.
• Monitoring/observability tools (Prometheus, Grafana).
• Understanding of DevOps and MLOps practices.
• Ability to translate requirements into scalable infrastructure.

Responsibilities

• Design, implement, and maintain data/ML platform infrastructure (ingestion, storage, processing, training).
• Ensure high availability, reliability, and uptime of the platform.
• Evolve vendor components into automated, multi-product ML platform.
• Build data workflows for dataset versioning, experiment tracking, and model lifecycle management.
• Enforce CI/CD, IaC, and automated workflows.
• Develop cloud-agnostic, containerized solutions across EU constraints.
• Optimize storage costs with tiered storage and retention strategies.
• Implement monitoring/alerting for pipelines and resources (Prometheus/Grafana).
• Ensure data governance, security, and compliance (access control, audit logs).
• Collaborate with data scientists, ML engineers, and IT to meet needs.
• Manage budgets, approvals, and cost controls.
• Contribute to runbooks/docs for the platform.

Skills

Python

CI/CD

DevOps

MLOps

Distributed systems

Cloud infrastructure

Data pipelines

Team collaboration

Education

MSc or equivalent

Tools

Terraform

MLflow

Feast

DVC

DataHub

AWS Sagemaker

Google Vertex AI

MongoDB

PostgreSQL

InfluxDB

Kafka

RabbitMQ

Docker

Kubernetes

GitLab CI

Prometheus

Grafana

Job description

We are expanding our team with a Data & ML Infrastructure Engineer who will take ownership of the platform that enables data ingestion, processing, storage, and machine learning workflows across multiple products. Operating at the intersection of data science, software engineering, and cloud infrastructure, you will build the foundations and ensure that our ML systems are scalable, reproducible, and production ready.

In this role, you will design, implement, and continuously improve a cloud-agnostic data and ML platform that supports multiple products and teams. You will be responsible for platform reliability, scalability, and cost-efficiency, while providing data scientists and ML engineers with the tools and infrastructure to efficiently develop, train, validate, and deploy machine learning models. You will serve as a technical bridge between the data science and IT/infrastructure teams, translating requirements into robust and maintainable systems.

This role includes ownership of an existing vendor-delivered platform and its evolution into a fully automated, reproducible, and multi-product ML platform.

Key Responsibilities:

• Design, implement, and maintain the data and ML platform infrastructure, including systems for data ingestion, storage, processing, and training.
• Ensure high availability, reliability, and uptime of the platform, as it is critical to the business value proposition.
• Maintain and evolve vendor-delivered platform components and ensure long-term maintainability and independence.
• Build and maintain data workflows, including tools for dataset versioning, experiment tracking, and model lifecycle management across multiple products.
• Enforce DevOps / MLOps practices, including CI/CD pipelines, Infrastructure-as-Code, and automated workflows.
• Develop cloud-agnostic and containerized solutions that can run across public and private cloud environments within EU constraints.
• Optimize data storage, lifecycle policies, and cost efficiency, including tiered storage and retention strategies.
• Implement and maintain monitoring and alerting systems (e.g., Prometheus, Grafana) for pipelines, infrastructure, and resource usage.
• Ensure data governance, security, and compliance, including access control, audit logging, and anonymization requirements.
• Collaborate closely with data scientists, ML engineers, and IT teams to deliver infrastructure that meets operational needs.
• Manage resource and cost controls, including budgets, approvals, and tracking.
• Contribute to documentation, operational runbooks, and onboarding materials for the platform.

Why Join Us?

• Join one of Europe’s most promising deep-tech scale-ups.
• Shape the future of a rapidly growing multidisciplinary R&D organization.
• Competitive salary and benefits package.
• Enjoy regular team activities and company events.
• Fresh team lunches provided3 times/ week.

Required Qualifications:

• MSc or equivalent with 5+ years’ industry experience in Computer Science, Data Engineering, Machine Learning, or a related field.
• Strong experience with data platforms, cloud infrastructure, and distributed systems (AWS, GCP, Azure).
• Proven experience with Infrastructure-as-Code (e.g., Terraform) and automated deployments.
• Experience with ML platforms and tools (MLflow, Feast, DVC, DataHub or equivalent).
• Experience with AWS Sagemaker or Google Vertex AI platforms.
• Experience with databases (MongoDB, PostgreSQL, InfluxDB or equivalent).
• Experience with message brokers and streaming systems (Kafka, RabbitMQ or equivalent).
• Hands-on experience with containerization (Docker/OCI) and orchestration (Kubernetes or similar).
• Experience with CI/CD systems (e.g., GitLab CI).
• Programming skills in Python.
• Experience with monitoring and observability tools (Prometheus, Grafana).
• Understanding of DevOps and MLOps practices.
• Ability to work across teams and translate requirements into scalable infrastructure solutions.

Nice to Have:

• Knowledge of data governance, IAM, and security frameworks.
• Experience with cost optimization and resource management in cloud environments.
• Familiarity with multi-tenant or multi-product platform architectures.
• Experience working with EU data compliance constraints in energy, maritime or defense.

Security & Compliance:

You’ll be working with clients in the defense and security sectors, obtaining a Certificate of No Objection issued by the AIVD (Dutch General Intelligence and Security Service) is mandatory. This means you will need to undergo a security screening. For more information, please refer to details on security screening.