MLOps Engineer (Machine Learning Platform) (m/f/d)

Machine Learning Operations Engineer (m/f/d)

We are currently supporting one of our established clients in recruiting a Machine Learning Operations Engineer to help shape and scale the infrastructure behind innovative, data-driven software solutions. In this role, you will work at the intersection of machine learning, software engineering, and scientific computing, building reliable and reproducible ML platforms that enable engineering teams and domain experts to turn complex data into meaningful, real-world outcomes.

The Role

• Design, develop, and maintain scalable ML platforms and automated workflows that streamline machine learning development and delivery.
• Take ownership of workflow orchestration, experiment tracking, and the management of datasets, models, and other ML artifacts.
• Establish standards and processes for versioning, traceability, reproducibility, and governance throughout the ML lifecycle.
• Implement validation and compatibility checks to ensure the reliability and consistency of models, data, and scientific outputs.
• Improve platform observability through structured logging, metrics, monitoring, and clear failure diagnostics.
• Develop robust recovery, rollback, and rerun mechanisms to improve platform stability and operational reliability.
• Identify recurring technical challenges and translate them into reusable tools, automation, and platform improvements.
• Collaborate closely with software engineers, machine learning specialists, scientists, and other domain experts to continuously improve ML development processes.

Your Profile

• Strong software engineering skills, particularly in Python, with experience building reliable, maintainable software systems.
• Practical experience with MLOps, ML platforms, workflow orchestration, experiment tracking, or artifact management.
• Hands-on experience with Docker, Kubernetes or similar technologies, and cloud environments such as AWS, Azure, or GCP.
• Solid understanding of CI/CD, automated workflows, observability, versioning, and reproducibility in production environments.
• Experience working with complex data pipelines or distributed systems, with a focus on reliability, traceability, and data integrity.
• A proactive, structured, and independent working style, with a strong sense of ownership and the ability to turn recurring problems into sustainable solutions.
• A degree in Computer Science, Engineering, Data Engineering, or a related technical field.
• Ability to work remotely with at least four hours of overlap with Central European Time.

Nice to have:

• Experience with workflow orchestration tools such as Flyte or Airflow, or with internal developer platforms.
• Familiarity with scientific computing, industrial ML, or collaboration with scientific and domain experts.

Your Benefits

• Meaningful technical impact: Help shape the platform infrastructure behind sophisticated software solutions with real-world applications.
• Modern technology environment: Work with contemporary cloud technologies, automation frameworks, and machine learning infrastructure.
• A role beyond model development: Focus on the systems, tools, and standards that enable ML teams to work more efficiently and reliably.
• Cross-functional collaboration: Work closely with engineers, machine learning specialists, and scientific experts in a collaborative environment.
• Ownership and autonomy: Take responsibility for key platform components and have the opportunity to turn your ideas into lasting technical improvements.
• Remote working: Enjoy the flexibility of a remote role while collaborating with an internationally oriented team.

If you are ready to build reliable, scalable ML infrastructure and make a tangible impact at the intersection of software engineering and applied science, we look forward to hearing from you!