Senior Consultant MLOps Engineering

📋 Job Overview

Join Comma as an MLOps Engineer, where you will take on multiple roles in customer projects, from technical expert to implementer and enabler, focusing on MLOps infrastructure and automation solutions.

🏢 About Comma

Comma is a company that works on innovative projects in the field of machine learning and automation.

🎯 The Role

As an MLOps Engineer, you will be responsible for the technical conception and implementation of MLOps infrastructures and automation solutions, building platforms and pipelines, and supporting data science teams in effectively utilizing MLOps platforms.

✅ Key Responsibilities

• Act as a technical expert/architect for MLOps, being the primary point of contact for technical conception and implementation of MLOps infrastructures and automation solutions.
• Build platforms and pipelines, configure tools, and write necessary code (IaC, CI/CD configurations, automation scripts).
• Help data science teams and other stakeholders effectively use MLOps platforms and successfully bring their machine learning models into production.
• Coordinate technical requirements at the interface between Data Science, Software Development (DevOps), IT Operations, and Security to integrate solutions.
• Keep track of current technologies and evaluate new developments in the field of MLOps and DevOps.
• Design, develop, and maintain MLOps platforms, including the design of technical infrastructure and toolchains (e.g., based on Kubeflow and Kubernetes) to support and automate the entire machine learning lifecycle.
• Support scalable deployment of machine learning models and automate their workflows through CI/CD pipelines (e.g., with GitLab CI/CD or GitHub Actions), including troubleshooting and technical support.
• Integrate and operate central MLOps tools like MLflow for experiment tracking, model registry, and deployment, and integrate them seamlessly into CI/CD processes.
• Implement GitOps practices (e.g., with Argo CD) for automated delivery of infrastructure and application components.
• Build and further develop monitoring and logging solutions to monitor the platform, pipeline executions, and the behavior of productive machine learning models.
• Actively shape DevSecOps processes, including container scanning, secret management, and access controls, working closely with security teams to integrate security into automated processes.