MLOps Engineer

Quick Facts

• Role: MLOps Engineer

Description

Design, implement, and maintain MLOps processes that support the development and production operation of machine learning models. You will automate the model lifecycle, build and maintain CI/CD pipelines for ML applications and models, and monitor model performance and prediction quality in production. You’ll collaborate with Data Science, Data Engineering, Software Engineering, and IT to ensure scalable, secure, reliable, and cost-optimized AI solutions.

Responsibilities

• Design and develop MLOps processes and the infrastructure supporting the ML model lifecycle
• Automate training, testing, deployment, and monitoring of ML models
• Build and maintain CI/CD pipelines for ML applications and models
• Implement versioning for code, data, models, and experiments
• Monitor model performance, prediction quality, and production environment stability
• Collaborate with Data Science and Engineering teams to move models from experimentation to production
• Manage cloud or on-prem infrastructure using Infrastructure as Code
• Ensure scalability, security, reliability, and cost optimization of ML solutions
• Create and update technical documentation and model deployment standards
• Identify and resolve production issues related to models and MLOps processes

Requirements

• Minimum 3 years of experience as an MLOps Engineer, DevOps Engineer, Machine Learning Engineer, or similar
• Practical Python knowledge and software development tooling
• Experience building and maintaining CI/CD pipelines
• Knowledge of containerization tools (Docker) and orchestration platforms (Kubernetes)
• Knowledge of one major cloud platform: AWS, Microsoft Azure, or Google Cloud
• Experience with tools for experiment and model management (e.g., MLflow, Kubeflow or similar)
• Knowledge of version control systems, especially Git
• Knowledge of monitoring, logging, and ensuring system reliability
• Knowledge of databases and data processing tools (additional advantage)
• Analytical thinking, problem-solving, and ability to work in a team
• Strong English knowledge to work with technical documentation and communicate in an international environment

Benefits

• Participate in the development of modern AI and machine learning solutions
• Work with an experienced team of specialists
• Influence technology choices and shape MLOps processes
• Access to modern tools and cloud infrastructure
• Professional growth through challenging projects and training
• Flexible work model with remote or hybrid options
• Stable employment and competitive compensation