Amazon
Principal Tech Ops Engineer, AWS Rack Manufacturing
$166K–$260K
Salt Lake City, UT · Principal · Full-time · Posted Oct 30, 2026
- Experience
- 10+ years of technical program management experience; basic qualifications also specify 5+ years of relevant hands-on systems engineering and administrative work and 5+ years working with external suppliers.
- Education
- Bachelor's degree in Industrial Engineering, Mechanical Engineering, or another STEM field.
About the job
Leads regional, multi-vendor manufacturing operations for AWS hardware, scaling production capacity, supplier performance, and manufacturing technology across sites.
What you'll do
- Own regional manufacturing strategy, capacity planning, supplier onboarding, and operational performance.
- Lead manufacturing transformation, including automation, liquid-cooled rack production, and setup of a Manufacturing Center of Excellence.
- Set operational metrics and drive continuous improvement, risk mitigation, cost control, and digital transformation across manufacturing sites.
What they ask for
- Bachelor's degree in Industrial Engineering, Mechanical Engineering, or another STEM field.
- 5+ years of relevant hands-on systems engineering and administrative work in networking, storage systems, or operating systems.
- Experience with Six Sigma, lean manufacturing, material handling equipment (MHE), or automation environments.
- 5+ years working with external suppliers.
Nice to have
- Master's degree or above in engineering, management, technology, operations, supply chain, or business administration; or experience developing and deploying LLMs on AI acceleration hardware, or computer architecture.
- Experience coordinating complex products with stringent technical requirements, development cycles, and schedules; knowledge of Six Sigma tools, Lean techniques, PMP, or similar standards.
Pay
165,700.00 - 260,000.00 USD annually (base salary)
Tools
Power BI, Python, SQL, Tableau, LLMs, GPUs, Neuron, TPU
AI focus
Champion AI-driven quality inspection and smart-factory innovation; preferred experience includes developing and deploying LLMs in production on GPUs, Neuron, TPU, or other AI acceleration hardware.
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