MLOps Engineer: Enterprise ML on Databricks & Azure

Dow; Kankakee (IL); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Competitive pay

Bonus opportunities

Total rewards program

Employee discounts

Job summary

Dow is seeking a Machine Learning Engineer to drive the enterprise MLOps framework across Databricks and Azure. You will lead architecture, deployment, and governance of ML assets, collaborating with data scientists, engineers, and cybersecurity partners to deliver reliable production solutions.

You will design pipelines, implement monitoring, and mentor teams in modern ML engineering practices while shaping the end-to-end lifecycle from development to production across multiple Dow locations.

Qualifications

• A bachelor’s degree or equivalent experience is required.
• 3+ years in machine learning, data science, software or data engineering.
• U.S. work authorization is required; no visa sponsorship is provided.

Responsibilities

• Own architecture and roadmap for the enterprise MLOps framework with a Databricks-first approach.
• Lead development of CI/CD, IaC, and testing patterns for ML code and data pipelines.
• Mentor teams on modern ML software engineering practices and production readiness.
• Design and monitor end-to-end ML pipelines from training to deployment.
• Collaborate with data scientists, engineers, and IT to securely integrate models into production.

Skills

MLOps Architecture

Python

PySpark

SQL

Databricks

Azure ML / Azure services

CI/CD

Model deployment

Education

Bachelor's degree

Tools

Databricks

Azure DevOps

MLflow

Unity Catalog

Spark

Job description

Dow is seeking a Machine Learning Engineer to drive the enterprise MLOps framework across Databricks and Azure. You will lead architecture, deployment, and governance of ML assets, collaborating with data scientists, engineers, and cybersecurity partners to deliver reliable production solutions.

You will design pipelines, implement monitoring, and mentor teams in modern ML engineering practices while shaping the end-to-end lifecycle from development to production across multiple Dow locations.