ML Ops Engineer

View full description

Requirements:

• Experience with tools like TensorFlow, PyTorch, MLflow, Kubeflow or similar
• Experience managing data pipelines (Airflow)
• Knowledge of DevOps practices (CI/CD, monitoring, automation)
• Proficiency in Python (with ML libraries) and Git.
• Experience with model monitoring solutions (e.g. Prometheus, Grafana).
• Experience with Apache Spark, Hadoop or other big data tools is a huge plus!
• Great communication skills in English (both written and spoken)
• Problem solver, team player with positive attitude!
• University degree in computer science, mathematics, natural sciences, or similar field and relevant working experience

Responsibilities:

• Design and develop pipelines for training and deploying ML models
• Automate and monitor ML processes, including version control and model updates
• Collaborate with Data Science and Data Engineering teams to integrate ML models into production environments
• Build and maintain systems for monitoring ML models to track performance and stability
• Optimize cloud infrastructure (GCP) for ML deployments
• Troubleshoot issues related to performance and availability of ML models in production
• Ensure CI/CD best practices for ML models