MLOps Engineer – AI/ML Production on AWS

Oxy; Georgia; On-site; USD 120,000 - 170,000; Full time

Job summary

Oxy is seeking a mid-career MLOps / AI Ops Engineer to support deployment, monitoring, and lifecycle management of ML and analytics solutions across upstream oil & gas operations. This role bridges data science, cloud engineering, and operations to ensure reliable, scalable AI systems in production.

You will design and maintain MLOps pipelines on AWS, implement CI/CD, model versioning, and observability, and collaborate with cross-functional teams to move models from development to production.

Qualifications

• 5+ years in data engineering, software engineering, MLOps, or AI Ops.
• Strong Python production ML workflow proficiency.
• Experience deploying ML models in production using AWS and container tech.

Responsibilities

• Design, build, and maintain MLOps pipelines and platforms for model training, deployment, monitoring, and retraining using AWS.
• Operationalize 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, infrastructure.

Skills

Python

AWS

CI/CD

Docker

Kubernetes

ML lifecycle management

Production ML

Tools

S3

EC2

SageMaker

CloudWatch

EKS/ECS

Terraform

Job description

Oxy is seeking a mid-career MLOps / AI Ops Engineer to support deployment, monitoring, and lifecycle management of ML and analytics solutions across upstream oil & gas operations. This role bridges data science, cloud engineering, and operations to ensure reliable, scalable AI systems in production.

You will design and maintain MLOps pipelines on AWS, implement CI/CD, model versioning, and observability, and collaborate with cross-functional teams to move models from development to production.