ML Ops Engineer

Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.

Implement model governance strategies—versioning, rollback, audit trails, and explainability.

Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).

Collaborate with software, IT, and data teams to ensure model integration and compliance.

Support continuous improvement of ML lifecycle management practices and tooling.

business trip to Kuwait

Strong software engineering background with ML Ops or DevOps exposure

Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.

Proficiency in CI/CD workflows and scripting for automation.

Familiarity with model monitoring, drift detection, and alerting frameworks.

Experience deploying Python-based ML models in production.

Understanding of Digital Oilfield environments or OT/IT integration.

Familiarity with cloud-native services—Azure is preferred.

Exposure to real-time data streaming (e.g., Kafka, IoT platforms).

Understanding of security, compliance, and data access protocols in enterprise environments.

Certifications:

Azure DevOps or Azure AI Engineer certification is a plus.

AWS DevOps Engineer certification is nice to have but not required