Machine Learning Engineer Cloud Infrastructure Specialist
3 weeks ago
As a global leader in industrial distribution, Grainger serves over 4.5 million customers worldwide with products delivered through innovative technology and deep customer relationships. With 2023 sales of $16.5 billion, we are dedicated to providing value for customers, fostering an engaging culture for team members, and driving strong financial results.
Salary and Benefits
We offer a competitive salary of approximately $170,000 per year, based on industry standards and market research. In addition to a comprehensive benefits package, including medical, dental, and vision insurance, retirement plan, and paid time off, we also provide opportunities for professional growth and development, including training programs and career advancement opportunities.
Job Description
As a Machine Learning Engineer Cloud Infrastructure Specialist, you will work collaboratively with machine learning, data engineering, network, security, and platform engineering teams to build core components of a scalable, self-service machine learning platform that powers customer-facing applications. You will play an important part in developing the tools and services that form the backbone of Grainger's AI-driven features leveraging methods in Deep Learning, Natural Language Processing / Generative AI, Computer Vision, and beyond. You will design, monitor, and improve cloud infrastructure solutions which support applications executing at scale, and ensure a rigorous deployment process using DevOps standards and mentor users in software development best practices.
Required Skills and Qualifications
The ideal candidate will possess a Bachelor's degree and 5+ years' relevant work experience or an equivalent combination of education and experience. Key qualifications include strong software engineering fundamentals, experience with Python, Golang, or similar language, working knowledge of cloud-based services, and expertise with IaC tools and patterns. Experience with DevOps or GitOps practices and tools, application monitoring and observability tools, and containerization technologies is highly desirable.
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