MLOps Engineer

Quick Facts

• Role: MLOps Engineer

Description

You will build and maintain an MLOps tool ecosystem with a strong focus on MLflow, including automating CI/CD processes for ML model lifecycles. The work covers production-grade Python code maintenance, container orchestration on OpenShift/OKD4, and performance troubleshooting for ML/AI environments. You will collaborate closely with Data Scientists to turn prototypes into production services while ensuring security, governance, and regulatory compliance for AI/ML systems.

Responsibilities

• Develop and configure the MLOps tooling ecosystem (especially MLflow)

• Automate CI/CD pipelines and the machine-learning model lifecycle (Jenkins, MLflow)

• Maintain, refactor, and extend production Python codebase

• Manage container orchestration platform and standardize model deployment processes (OpenShift, OKD4)

• Diagnose, optimize, and troubleshoot production ML/AI environments for performance

• Implement security, governance, and regulatory compliance standards for AI/ML systems

• Work with Data Scientists, ML Engineers, and analysts to move prototypes to production services

• Provide analytical/technical support for new projects and maintain consistent technical and process documentation

Requirements

• At least 3 years of commercial experience in Machine Learning/DevOps or MLOps

• Very good Python skills with at least 3 years of programming experience (preferably in ML project work)

• Practical knowledge of ML infrastructure architecture for deployments (data flow, scalability, model lifecycle)

• Experience managing self-hosted MLOps solutions (MLflow, Kubeflow or similar)

• Proficiency with Docker, container platforms (Kubernetes, OpenShift/OKD4), and deployment automation tools (Jenkins, GitLab CI, Argo CD)

• Experience with huge datasets and NLP solutions

• Business-oriented approach: ability to identify user needs and translate them into practical advanced analytics and AI solutions

Benefits

• Close collaboration with ML experts and Data Scientists

• Full support from specialists from the DevOps and MLOps boundary areas

• Opportunity to develop and mature end-to-end ML/AI production capabilities