Design, build, and maintain end-to-end ML pipelines from data ingestion to model deployment and monitoring
Collaborate with data scientists and cloud engineers to productionize ML models and establish best practices
Develop and maintain model serving infrastructure with focus on scalability, reliability, and low latency
Manage ML experiment tracking, model versioning, and model registry systems
Monitor model performance in production and implement alerting systems
Implement security best practices for ML systems and ensure compliance with data governance policies
Document MLOps processes, architecture decisions, and runbooks.

Bachelor's degree or higher in Computer Science, Data Science, Engineering or related field
5+ years of experience in MLOps, DevOps, or related production ML roles
Experience deploying ML/AI prototypes into production
Excellent communication skills and ability to work cross-functionally
Self-motivated with ability to work independently and manage multiple priorities
Passion for automation and building reliable systems
Detail-oriented with focus on code quality and best practices
Hands-on experience with modern ML frameworks (PyTorch, TensorFlow, or similar)
Familiarity with LLM frameworks, vector databases, and RAG architectures
Strong experience with cloud platforms (AWS, GCP, or Azure)
Experience with cloud data warehouses, SQL/NoSQL databases and real-time data pipelines
Hands-on experience with container orchestration (Kubernetes, ECS, or similar)
Proficiency with CI/CD pipelines, Infrastructure as Code, and version control
Strong understanding of REST API design and implementation.

25 days of annual vacation leave plus 10 days of fully paid sick leave.
Fringes benefits: meal allowance, work-from-home allowance, gym allowance, private health insurance plan for yourself and 2 children.
1 day per month to tackle challenges inside the project, or test new ideas.
Tech training and human skills training and, budget for conferences.
Opportunity to travel and collaborate in our various office locations for workshops or team-building events.
A dynamic work environment that highly fosters a sense of fun while maintaining a productive atmosphere.
A competitive salary.

Machine Learning Ops Engineer

Key skills