Senior ML Engineer: Production ML & Ops (Hybrid, Chicago)

Grainger Corporate Services LLC; Chicago (IL); Hybrid; USD 134,000 - 224,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Paid time off

401(k) match

Tuition reimbursement

Employee discounts

Maternity/paternity benefits

Job summary

Grainger Corporate Services LLC in Chicago, IL is seeking an experienced ML/OR engineer to design scalable, production-grade data solutions. You will lead automated pipelines for data analysis, model training, validation, deployment, and build reusable tools for experimentation and delivery.

You will collaborate with cross-functional teams to align analytics with business needs, implement ETL pipelines, leverage AWS, Python, SQL, and modern data tools, and mentor teammates on architecture and

Qualifications

• Bachelor’s degree in CS/Engineering or related field (required).
• Master’s degree in related technical field or equivalent practical experience (preferred).
• 3+ years of production ML, optimization, modern data tools, and scalable engineering practices (required).
• Experience with AWS services including S3, ECR, Secrets Manager.

Responsibilities

• Design and deliver scalable, production-grade ML and OR solutions.
• Lead automated data pipelines for data analysis, model training, validation, and deployment.
• Collaborate cross-functionally to align analytics with business needs.

Skills

AWS

Python

Gurobi

SQL

Kubernetes

Docker

Databricks

PyTorch

scikit-learn

GitHub

GitHub Actions

Airflow

Astronomer

S3

ECR

Secrets Manager

Snowflake

DuckDB

SQL (time series)

Education

Bachelor’s degree

Master’s degree

Tools

Gurobi

Snowflake

DuckDB

Job description

Grainger Corporate Services LLC in Chicago, IL is seeking an experienced ML/OR engineer to design scalable, production-grade data solutions. You will lead automated pipelines for data analysis, model training, validation, deployment, and build reusable tools for experimentation and delivery.

You will collaborate with cross-functional teams to align analytics with business needs, implement ETL pipelines, leverage AWS, Python, SQL, and modern data tools, and mentor teammates on architecture and