Advisor IT Systems - AI/ML Ops

Overview In this role, you will design and operate MLOps pipelines and platforms on AWS to enable scalable, reliable AI solutions for upstream oil and gas operations. You’ll bridge data science, cloud engineering, and operations to productionize models for optimization and analytics, with emphasis on governance, observability, and secure deployment. You will collaborate with cross-functional teams to move models from development to production and troubleshoot production data and model issues. This is a hands-on role at a company focused on responsible energy leadership and low‑carbon technologies. Responsibilities Design, build, and maintain MLOps pipelines and platforms for model training, deployment, monitoring, and retraining using AWSOperationalize ML models for upstream use cases (production optimization, subsurface modeling, drilling analytics)Implement CI/CD, model versioning, experiment tracking, and performance monitoring Collaborate with data scientists, data engineers, and domain experts to move models from development to production Ensure reliability, observability, governance, and compliance of ML systems Troubleshoot production issues related to data, models, and infrastructure Key requirements 5+ years of experience in data engineering, software engineering, MLOps, or AI Ops Strong Python production-grade ML workflow skills Hands-on AWS experience (S3, EC2, EKS/ECS, Sage Maker, Lambda, Cloud Watch)Experience deploying ML models in production Familiarity with CI/CD, Docker, and Kubernetes Understanding of ML lifecycle management, model monitoring, and data driftcross-functional collaborationproblem solvingfocus on reliability and governance PythonAWS (S3, EC2, EKS/ECS, Sage Maker, Lambda, Cloud Watch)ML lifecycle management