Job Summary:

We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.

The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Key Responsibilities:

• 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.

Required Skills and Experience:

• 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