MLOps Platform Engineer (Azure & Databricks)

The Hershey Company; Dallas (TX); On-site; USD 140,000 - 200,000; Full time

Job summary

The Hershey Company is seeking a Staff Data Engineer, MLOps in Dallas, TX to lead the design, build, and optimization of Hershey’s ML operations platform. You will enable data science teams to develop, deploy, monitor, and govern models at enterprise scale within the Platform Engineering group.

Responsibilities include building the end-to-end MLOps stack on Azure and Databricks, CI/CD pipelines, model serving patterns, governance, and cost-aware infrastructure.

Qualifications

• 5–10 years in software, ML, data platform, or infra engineering with 3+ years building/operating ML pipelines.
• Hands-on with Azure and Databricks in a production ML context.
• Strong Python and SQL; experience with ML frameworks and API development.

Responsibilities

• Design and maintain the end-to-end MLOps platform on Azure and Databricks.
• Build and optimize CI/CD pipelines for automated model training and deployment.
• Implement model serving patterns (batch/real-time) with safe rollout strategies.
• Build monitoring for data/drift, performance, and retraining triggers.
• Enforce ML governance including versioning, lineage, artifacts, and audit trails.
• Collaborate with Data Scientists to productionize models and mentor engineers.

Skills

MLOps & ML Engineering

Cloud & Platforms

Programming & Development

Collaboration & Communication

Education

Bachelor’s degree in CS/Engineering/Data Science

Master’s degree preferred

Tools

MLflow

Databricks

Azure Machine Learning

Terraform/Bicep

Job description

The Hershey Company is seeking a Staff Data Engineer, MLOps in Dallas, TX to lead the design, build, and optimization of Hershey’s ML operations platform. You will enable data science teams to develop, deploy, monitor, and govern models at enterprise scale within the Platform Engineering group.

Responsibilities include building the end-to-end MLOps stack on Azure and Databricks, CI/CD pipelines, model serving patterns, governance, and cost-aware infrastructure.