Senior MLOps & DevOps Engineer at Medis Medical Imaging Systems B.V.

Leiden, South Holland, Netherlands -

Full Time

Start Date

Immediate

Expiry Date

17 Jun, 26

Salary

0.0

Posted On

19 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

DevOps, MLOps, ML Platform Engineering, Python, MLflow, Airflow, SageMaker, Azure ML, AWS, Bash, PowerShell, GitHub Actions, Jenkins, Kubernetes, Prometheus, Grafana

Industry

Medical Equipment Manufacturing

Description

Who are we? Medis Medical Imaging is driven by a bold ambition: to transform complex cardiovascular imaging into intuitive software that enables medical professionals to work smarter, faster, and with greater confidence. For over 30 years, Medis has pioneered advanced imaging tools, making them accessible to researchers and clinicians worldwide. Today, we build on this legacy with a forward-looking roadmap powered by AI innovation. Working at Medis is both purposeful and inspiring. You'll be part of a fast-paced, innovation-led company with a global presence, rooted in a strong heritage and a pioneering spirit. Our culture is built by smart, hands-on people who take ownership and care deeply about their work and each other. We value connection, curiosity, and collaboration. We take pride in delivering technology that truly makes a difference. ㅤ The role As a Senior MLOps & DevOps Engineer, you will bridge clinical data and machine learning development workflows with product-grade software delivery and operational deployment. This role requires a highly self-directed engineer who takes ownership, operates with a high degree of autonomy, drives system design and technical direction, and over time grows into leading a small team of engineers. Working closely with Applied Research, validation, software development, testing, security, and operations teams, in this hands-on role you ensure secure, compliant, and reproducible AI-enabled releases within Medis products - spanning both ML lifecycle pipelines and DevOps delivery infrastructure. ㅤ What you will do Strategic tasks Drive system design and technical direction Define and implement best practices for integrating ML workflows into product delivery pipelines Establish standards for dataset and model versioning, reproducibility, and traceability Ensure ML and product delivery pipelines align with cybersecurity and medical device compliance expectations (IEC 62304, ISO 13485, GDPR, HIPAA, etc.) Contribute to platform roadmap and delivery planning with R&D leadership ㅤ Operational tasks Review, build, and maintain ML platform components for training, deployment, and inference Develop tooling that enables ML practitioners to experiment, version, deploy, and monitor models reliably Implement automated evaluation, validation, and promotion gates for AI models moving from research into product Design and maintain CI/CD pipelines for ML models and software Extend DevOps infrastructure with secure-by-design controls (IAM, secrets, scanning) Implement infrastructure-as-code for scalable and auditable environments Build observability for ML and product systems including telemetry pipelines, drift detection, alerting, and reliability monitoring Implement and support DTAP (Development, Test, Acceptance, Production) environments for both product software and ML-enabled components Ensure controlled promotion of releases and models across DTAP stages Integrate pipelines with Medis’ SaaS toolchain and cloud platforms (e.g., Atlassian stack, cloud ML services, monitoring platforms) Support hybrid infrastructure setups (on-premises and cloud) 5–10 years’ experience in DevOps, MLOps, or ML Platform Engineering Strong Python development and debugging skills Hands-on experience with ML lifecycle tooling (e.g., MLflow, Airflow, SageMaker, Azure ML) Experience with at least one major cloud platform (AWS preferred) Proficient in automation scripting (Bash, PowerShell) and experienced in designing CI/CD workflows with GitHub Actions, Jenkins, or similar platforms Expertise in containerization and orchestration (Kubernetes) for packaging, deploying, and scaling both ML workloads and production software services Experience with observability, monitoring, and logging using the Prometheus stack (Prometheus, Grafana, Alertmanager) and centralized logging solutions (ELK, Loki) Experience implementing CI/CD pipelines with automated testing and validation for ML-enabled systems Understanding networking, security, and compliant deployment in the regulated medical environment Experience with Infrastructure-as-Code (Terraform/CloudFormation, etc.) Experience working in IEC 62304, ISO 13485 regulated environments A rewarding and competitive compensation package, with both fixed and variable components A pension plan with strong employer contribution Flexibility to purchase additional leave days A diverse, collaborative, and international team culture with colleagues across Europe, the US, and Asia The chance to work on innovative solutions that truly impact patient care and the future of cardiovascular healthcare A hybrid work model – 2 days per week in our Leiden office, with flexibility for the rest

Responsibilities

This role involves driving system design and technical direction, defining best practices for integrating ML workflows into product delivery pipelines, and ensuring compliance for AI-enabled releases. Operationally, the engineer will build and maintain ML platform components, develop tooling for ML practitioners, and design CI/CD pipelines for models and software.