ML Ops Engineer – Digital Oilfield

Job Brief:

Are you the ML Ops authority responsible for turning proof-of-concept models into robust, production-grade, highly available solutions? Do you have a passion for CI/CD, pipeline automation, and model traceability?

• If you are the engineer who can develop and maintain end-to-end ML pipelines (preprocessing, training, deployment, and monitoring) with high standards for reliability...
• If you are the master of tools like Kubernetes, cloud platforms (Azure/AWS), and modern ML Ops frameworks...

Then secure your spot in this critical role ensuring our client’s ML solutions deliver continuous, reliable value.

Key Responsibilities:

• Design and implement scalable ML pipelines for digital oilfield data processing and automation.
• Develop and maintain CI/CD frameworks for model training, validation, and deployment.
• Collaborate with data scientists and domain experts to ensure reliable, production-grade model operations.
• Manage and monitor ML models using tools such as MLflow, Kubeflow, or Airflow.
• Implement best practices for data versioning, containerization (Docker), and orchestration (Kubernetes).
• Ensure compliance with data governance, security, and performance standards.
• Drive automation and performance optimization across the entire ML lifecycle.

Required Qualification / Experience / Skills:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.
• Minimum 10 years of experience in ML Ops or data engineering, preferably within the oil & gas sector.
• Expertise in Python, Linux, Docker, and Kubernetes.
• Strong understanding of CI/CD tools (GitLab CI, Jenkins) and cloud environments (AWS, Azure).
• Proven experience deploying ML models in production for real-time analytics.
• Familiarity with time-series data, IoT systems, and SCADA integration.
• Excellent communication, documentation, and troubleshooting skills.

Job Location: Remote

Type of Employment: Permanent / Full time

Salary: Negotiable (based on experience)

What you can expect from the employer:

• Competitive compensation based on experience.
• Exposure to digital transformation projects in the energy industry.
• Remote work flexibility and supportive work environment.
• Continuous learning and career advancement opportunities.

Job Types: Full-time, Permanent

Application Question(s):

• Do you have experience in ML Ops frameworks and deployment pipelines?
• Have you worked in digital oilfield or industrial automation environments?
• Are you experienced with Docker, Kubernetes, and cloud-based ML systems?
• Do you have 10 years of relevant experience in ML Ops or related fields?