ML Ops Engineer I - Production ML Pipelines

Murphy USA Inc.; El Dorado (AR); On-site; USD 73,000 - 94,000; Full time

Job summary

Murphy USA is seeking a Data Science MLOps Associate based in El Dorado, AR to bridge data science, data engineering, and cloud operations. You will design, build, deploy, monitor, and improve ML systems across the lifecycle, including pipelines, experimentation, and CI/CD, ensuring production readiness and governance.

You will collaborate with data scientists, engineers, security, and business teams to deliver scalable, secure, observable solutions, with strong emphasis on reproducibility and

Qualifications

• Experience building and productionizing data science or ML solutions.
• Proficiency in Python, SQL, source control, testing, and software engineering practices.
• Experience with cloud-based analytics platforms, preferably Azure and Databricks.
• Experience with MLOps/DevOps: CI/CD, automated testing, artifact and environment management.
• Knowledge of ML concepts: model development, experiment tracking, versioning, deployment and monitoring.

Responsibilities

• Design, build, and improve production-ready MLOps capabilities across the ML lifecycle.
• Collaborate with data scientists to productionize notebooks, models, and pipelines.
• Develop scalable data and feature pipelines using Python, SQL, Databricks, and cloud services.
• Create CI/CD pipelines for ML and analytics, including testing and deployment workflows.
• Implement model deployment patterns: batch, APIs, streaming, or embedded analytics.
• Establish monitoring and observability for models and data quality.

Skills

Python

SQL

Cloud platforms

CI/CD

DevOps practices

Model deployment

Data pipelines

Observability

Communication

Education

Four-year degree

Tools

Azure Databricks

Docker

Kubernetes

MLflow

GitHub

Terraform

Job description

Murphy USA is seeking a Data Science MLOps Associate based in El Dorado, AR to bridge data science, data engineering, and cloud operations. You will design, build, deploy, monitor, and improve ML systems across the lifecycle, including pipelines, experimentation, and CI/CD, ensuring production readiness and governance.

You will collaborate with data scientists, engineers, security, and business teams to deliver scalable, secure, observable solutions, with strong emphasis on reproducibility and