MLOps Engineer

We are looking for an MLOps Engineer to build and maintain machine learning systems across the organization and join the software division at IAI’s ELTA.

What will You do?

• Planning, developing, and managing infrastructure for the deployment and monitoring of machine learning models.
• Building and maintaining Big Data infrastructure using standard tools.
• Building and maintaining CI/CD/CT pipelines for model training and testing.
• Ensuring compliance with privacy and data security standards.
• Monitoring deployed models, troubleshooting issues, and ensuring consistent performance over time.
• Developing tools to automate data preparation, feature engineering, and model training processes.
• Implementing version control and model management practices to ensure reproducibility and auditability.
• Supporting the training and mentoring of engineers and data experts in MLOps best practices.

What we’re looking for?

• Relevant academic degree
• At least 2 years of experience in MLOps, machine learning, or another relevant field – required
• Proficiency in Python and relevant ML libraries (TensorFlow, PyTorch, scikit-learn) –required
• In-depth knowledge of DevOps tools such as Docker, Podman, Kubernetes, Jenkins, Terraform, Ansible – required
• Practical experience in CI/CD and systems (GitHub / GitLab, Azure DevOps), Git –required
• Understanding of model deployment processes, monitoring, and performance optimization – required
• Familiarity with data storage systems (SQL, NoSQL, Data Lakehouse) – required
• Experience with cloud platforms (GCP, AWS, Azure) and cloud resource management for ML – advantage
• Experience with MLOps tools (ClearML, Kubeflow, MLflow) – advantage
• Knowledge of ML frameworks (TensorFlow Serving, TorchServe, Seldon) –advantage
• Experience with Apache Airflow – advantage
• Familiarity with monitoring tools (Prometheus, Grafana) – advantage
• Experience with Data Lakehouse systems (Cloudera, Hive, Spark, Kafka) – advantage

Good to know:

The work includes close collaboration with data scientists, software engineers, and IT teams to establish and manage AI and Big Data infrastructures in development and production environments.