MLOps Engineer

Job summary

The Senior MLOps Engineer industrializes and operates machine learning models at scale, building and evolving a platform for Data Science and Machine Learning teams. The role automates model training, validation, deployment, and continuous training; manages production infrastructure and observability across AWS and GCP using Python, MLflow, Terraform, Docker, and Kubernetes.

Qualifications

• At least 3 years of experience in MLOps, ML Platform Engineering, or DevOps applied to machine learning.
• Hands-on experience putting machine learning models into production.
• Strong Python skills.
• Solid cloud experience with AWS and/or GCP.
• Proficiency with Terraform, Docker, and Kubernetes.
• Experience with MLflow and model lifecycle management.
• Understanding of monitoring, performance, and scalability for production models.
• Experience with Databricks, Airflow, Feature Stores, or GPU infrastructure is an advantage.
• Professional proficiency in English.

Responsibilities

• Design and industrialize end-to-end MLOps pipelines.
• Automate model training, validation, deployment, and continuous training.
• Deploy and operate machine learning models in production.
• Manage model lifecycles using MLflow, Model Registry, and related tools.
• Build and maintain infrastructure for machine learning and GPU workloads.
• Deploy and administer containerized environments with Docker and Kubernetes.
• Automate infrastructure provisioning with Terraform.
• Implement CI/CD pipelines for machine learning workloads.
• Support model monitoring, drift detection, and platform observability.
• Optimize infrastructure performance, scalability, and costs.
• Help Data Scientists and ML Engineers industrialize their models.
• Document platform practices and contribute to continuous improvement.

Skills

Languages

Industry

Contract length

Relocation