Lead Machine Learning Operations Engineer IV

About Grainger:

Grainger is a leading broad line distributor with operations primarily in North America, Japan and the United Kingdom. We achieve our purpose, We Keep the World Working®, by serving more than 4.5 million customers with a wide range of products that keep their operations running and their people safe. Grainger also delivers services and solutions, such as technical support and inventory management, to save customers time and money.

We're looking for passionate people who can move our company forward. As one of the 100 Best Companies to Work For, we have a welcoming workplace where you can build a career for yourself while fulfilling our purpose to keep the world working. We embrace new ways of thinking and recognize everyone is an individual. Find your way with Grainger today.

Position Details:

Responsibilities:

• Take offline models data scientists build and turn them into a real machine learning production system.
• Use AWS Sagemaker tools and services to handle machine learning training and inference
• Perform ML Model Training and integrate Machine Learning model with existing application/workflow
• Use IAC tools like Terraform to create ML Infrastructure and assist senior ML Engineer as required
• Prepare documentation for the team to assist them in using ML systems for model development
• Apply ML Engineering principles and best practices to machine learning prototypes, including CI/CD, automation, etc.
• Support model development, with an emphasis on auditability, versioning, and data security
• Facilitate the development and deployment of proof-of-concept machine learning systems

Requirements:

• Bachelor's or Master's degree in a field such as Computer Science, Engineering or related field
• 4+ years' experience maintaining ML systems using AWS Sagemaker Machine Learning platform
• Experience building end-to-end systems as a lead MLOps Engineer
• Strong ML engineering skills in complex systems
• Fluency in Python and IAC like Terraform(preferred) or Cloudformation to create ML pipelines/ infrastructure
• Experience working in cloud computing (preferably AWS) and database systems.
• Experience building custom integrations between cloud-based systems using APIs
• Experience creating docker containers and using containers to train deep learning models in cloud computing environments
• Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.)
• Exposure to machine learning methodology and best practices
• Experience leading junior MLOps resources

Rewards and Benefits:

With benefits starting day one, Grainger is committed to your safety, health and wellbeing. Our programs provide choice to meet our team members' individual needs. Check out some of the rewards available to you at Grainger.

• Medical, dental, vision, and life insurance coverage starts day one
• Paid time off (PTO) days and 6 company holidays per year
• 6% 401(k) company contribution each pay period
• Education assistance, including financial counseling, tuition reimbursement and low-cost degree options
• Employee discounts, parental leave, and more

DEI Statement

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal opportunity workplace.