DevOps/MLOps Engineer

Holisticon Connect is a division within NEXER GROUP, a custom software development company started in Poland in 2017 with over 140 people. The team works with world-renowned brands from Scandinavia, the UK, and Western Europe. The role supports development and scaling of internal machine learning tooling for large-scale image analysis in the medical domain, working at the intersection of machine learning, data engineering, and DevOps to build robust, production-ready, and compliant solutions in a regulated environment.

• At least 5 years of professional experience in DevOps, MLOps, or a similar role.
• Hands-on experience building and deploying machine learning systems using frameworks such as PyTorch, TensorFlow, or JAX (excluding hosted LLM APIs).
• Experience designing and maintaining data processing pipelines, ideally for image data or similar domains.
• Understanding of the full MLOps lifecycle: model training, validation, deployment, and monitoring.
• Excellent Python skills with clean, well-tested, and maintainable code.
• Application of software engineering best practices including testing, code quality, documentation, and performance.
• Solid experience with Linux environments, Git-based workflows, and CI/CD (e.g., GitHub Actions).
• Hands-on experience with containerization (Docker) and orchestration (Kubernetes).
• Experience with MLOps tools such as MLflow or Weights & Biases.

Nice to have:

• Experience with workflow orchestration platforms (e.g., Argo Workflows, Kubeflow).
• Familiarity with cloud infrastructure, especially AWS.
• Experience with distributed systems or microservices architectures.
• Exposure to computer vision (e.g., OpenCV or similar).
• Basic knowledge of TypeScript, Node.js, JavaScript.
• Experience working in regulated environments (e.g., healthcare, pharma, medtech).

• Support the development and scaling of internal machine learning tooling for large-scale image analysis in the medical domain.
• Work at the intersection of machine learning, data engineering, and DevOps.
• Transform experimental ML workflows into robust, production-ready, and compliant solutions.
• Collaborate closely with data scientists, software engineers, and domain experts.
• Build and maintain end-to-end ML pipelines ensuring scalability, reproducibility, and high performance in a regulated environment.