MLOps Engineer

The Software Division at ELTA Systems is seeking an MLOps Engineer.

The role involves designing, developing, and managing infrastructure for deploying and monitoring machine learning models. You will collaborate closely with Data Scientists, Software Engineers, developers, and architects to ensure stable and efficient model training.

You will build and maintain Big Data infrastructure using industry-standard tools, as well as develop and manage CI/CD/CT pipelines for model training and testing. The position includes ensuring data privacy and security compliance, monitoring deployed models, and troubleshooting to maintain consistent performance over time.

Additionally, you will develop automation tools for data preparation, feature engineering, and model training processes, implement version control and model management practices for reproducibility and auditability, and support mentoring and knowledge sharing of MLOps best practices across teams. Staying up to date with the latest trends in AI, Big Data, machine learning, and cloud infrastructure is an integral part of the role.

What We’re Looking For

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

Nice to Know

We are looking for an MLOps Engineer to build and maintain end-to-end machine learning systems at scale. The role involves close collaboration with Data Scientists, Software Engineers, and IT teams to establish and manage AI and Big Data infrastructures across both development and production environments