ML Engineer II, Operations — Production ML on Azure

TECHTRONIC CORDLESS GP; United States; On-site; USD 110,000 - 150,000; Full time

Benefits offered by this job

Health, dental and vision insurance

401(k) savings plan

Education assistance

On-site wellness, fitness center, food

Job summary

Milwaukee Tool is seeking a Machine Learning Engineer II to design, develop, and deploy ML solutions that improve manufacturing and service across our global operations.

You will collaborate with operations, quality, supply chain, engineering, and service teams, managing the full ML lifecycle from data engineering to deployment and monitoring on Azure and Databricks.

The role requires strong communication, problem solving, and willingness to work with international teams and travel up to 20%.

Qualifications

• Bachelor of Science Degree in Computer Science, Computer Engineering, Electrical Engineering or other scientific or engineering discipline.

Responsibilities

• Design, develop, and deploy machine learning solutions to improve manufacturing and services.
• Collaborate with operations, quality, supply chain, engineering, and service teams.
• Own full ML lifecycle from data engineering to deployment and monitoring on Azure and Databricks.
• Partner with Global and Service Teams to deploy and validate ML solutions in operational environments.
• Deliver production-ready ML solutions with measurable value.

Skills

Machine learning

Python

Spark

SQL

Data analysis

Communication

Cross-functional collaboration

Problem solving

Teamwork

Cloud platforms

Education

Bachelor of Science in CS/CE or related

Master’s degree preferred in ML or related field

Tools

PyTorch

TensorFlow

Keras

MLFlow

Azure

Databricks

Docker

Linux

Job description

Milwaukee Tool is seeking a Machine Learning Engineer II to design, develop, and deploy ML solutions that improve manufacturing and service across our global operations.

You will collaborate with operations, quality, supply chain, engineering, and service teams, managing the full ML lifecycle from data engineering to deployment and monitoring on Azure and Databricks.

The role requires strong communication, problem solving, and willingness to work with international teams and travel up to 20%.