Senior Machine Learning Engineer (MLOps)

Quick Facts

• Role: Senior Machine Learning Engineer (MLOps)

• Focus: Delivering and operating ML projects reliably in production

Description

Senior ML Engineer responsible for taking machine learning projects from design through stable production operation. You will shape ML system architecture, select technologies considering performance, complexity, operational risks, and costs, and expand the organization’s MLOps tooling (shared libraries, deployment templates, and monitoring practices). The role emphasizes continuous improvement of observability, data quality, operational metrics, and incident handling for ML services.

Responsibilities

• Provide technical ownership for ML projects end-to-end (from design to stable production operations)

• Design architectures for ML systems and choose appropriate technologies

• Develop the MLOps platform, shared libraries, service templates, and deployment/monitoring procedures

• Continuously improve observability of ML services, data quality, operational metrics, and incident solutions

• Collaborate with platform and DevOps teams to ensure production reliability

• Help ML colleagues translate models and prototypes into maintainable production systems

• Lead technical reviews, set standards, and share knowledge across the team

• Identify opportunities to simplify, standardize, and run solutions more effectively

Requirements

• Very strong Python knowledge and experience building maintainable production applications

• Experience designing architecture for distributed and/or data-oriented systems

• Practical experience with Docker, Kubernetes, and Helm

• Experience deploying, monitoring, and operating production services

• Knowledge of MLOps principles and the ML solution lifecycle (data/experiments, evaluation, versioning, deployment with monitoring)

• Ability to assess technical options and clearly explain benefits, risks, and costs

• Independence and ability to take ownership of outcomes

• Willingness to constructively challenge requirements and find solutions for the real problem

• Good communication with ML specialists, developers, platform teams, and business stakeholders

• Ability to work with technical documentation in English

Benefits

• Direct impact on ML systems supporting a key business area

• Technical challenges involving large data volumes and production operations

• Opportunity to influence architecture, the MLOps platform, and how ML projects are created and run

• Collaboration with experienced ML specialists and developers

• Space to advocate pragmatic solutions and change what doesn’t make technical or economic sense

Compensation & Perks

• Lunch contribution: 180 CZK/day

• 5 weeks of vacation

• 3 sick days/year

• Partial home office option

• Professional training and education

• Pension contribution

• Cafeteria benefits

• Contribution for children’s education at ZŠ Via Beroun

• On-site gym

• Telemedicine, psychological care, and legal advice

• Friendly team environment in Beroun (about 20 minutes from Nové Butovice metro station)