Advisor IT Systems

This role designs, builds, and maintains MLOps pipelines for deploying and monitoring machine learning solutions across upstream Oil & Gas use cases. You will help operationalize models, implement CI/CD and monitoring, and ensure governance and compliance in production AI systems.

• Design, build, and maintain MLOps pipelines and platforms for training/deployment/monitoring/retraining using AWS
• Operationalize ML models for upstream use cases such as production optimization, subsurface modeling, and drilling analytics
• Implement CI/CD, model versioning, experiment tracking, and performance monitoring
• Collaborate with data scientists, data engineers, and domain experts to move models to production
• Ensure reliability, observability, governance, and compliance; troubleshoot production issues

You will run end-to-end ML lifecycle management using AWS services such as S3, EC2, EKS/ECS, SageMaker, Lambda, and CloudWatch. The stack includes CI/CD, model versioning, experiment tracking, Docker, and Kubernetes, with a strong focus on observability, governance, and data drift monitoring.

The ideal candidate is a mid-level MLOps / AI Ops engineer with 5+ years building and operating machine learning systems in production. They have strong AWS experience (S3, EC2, EKS/ECS, SageMaker, Lambda, CloudWatch) and hands-on skills with CI/CD, Docker, and Kubernetes, plus strong Python capability for ML workflows.