Amazon Web Services Hardware Engineering team is looking for experienced professionals to help build the worlds premier cloud computing platform. Our Manufacturing Automation Design & Engineering team designs, validates, and deploys automation solutions robotics, cobots, vision inspection, AI-powered quality systems, and automated assembly workflows across contract manufacturer sites worldwide. About the Role: You will architect the controls backbone and intelligent inspection strategy that makes every automated station self-verifying. This means defining how PLCs, sensors, cameras, AOI algorithms, and MES data connections integrate across every automation cell creating a standardized, replicable controls and inspection package that deploys across all CM sites without re-engineering. You combine deep expertise in machine vision (AI-based defect classification, force sensing, AOI model training) with industrial controls architecture (PLC platforms, safety systems, network protocols). Your work turns a mechanical robot into an intelligent quality gate catching defects at the point of assembly rather than at test or in the datacenter. Our scope covers all server and rack assembly across all architectures and platforms compute, AI/ ML accelerators, storage, and networking. We design intelligent, predictive-analytics-driven production systems where every station is sensor-driven, AI-optimized, and self-correcting turning assembly lines into programmable, data-generating platforms that scale capacity without scaling headcount. This role requires the engineer to operate in a fast-paced, multi-platform international manufacturing environment with travel to contract manufacturer sites (up to 2530% travel anticipated). Key job responsibilities Architect controls standard for all automation cells: PLC platform selection, network protocol, sensor architecture, safety system design Develop and optimize AOI inspection programs per product validated, version-controlled, transferable across sites Drive false call rate reduction through iterative program tuning and AI-assisted defect classification Design sensor integration architecture connecting automation equipment to MES (i. Factory) for 100% digital traceability Create standardized controls packages (PLC logic HMI sensor configs) replicable across all CM sites Perform equipment qualification, Gage R&R studies, and acceptance testing for vision/inspection systems Define controls safety architecture (E-stops, interlocks, light curtains) per ISO 13849 / IEC 62443 Partner with Central Quality on inspection acceptance criteria and with MES teams on data integration requirements. About the team. We are building a centralized automation Center of Excellence that owns the process specification and IP for how AWS hardware gets assembled. The Manufacturing Automation Design & Engineering team is responsible for engineering defining automated process requirements, designing and proving solutions at proof-of-concept, and maintaining AWS-owned intellectual property that scales globally. We work within a collaborative framework across Hardware Engineering (product design for automation), Manufacturing Automation Design & Engineering (process specification and validation), and Manufacturing Operations (deployment and scaling) ensuring automation is engineered once, proven once, and deployed everywhere. Basic Qualifications- BS/ MS Electrical Engineering, Computer Engineering, Controls Engineering, or equivalent- 5 years in industrial controls AND machine vision/automated optical inspection- Proficiency in PLC programming (Allen-Bradley, Siemens, or Beckhoff)- Experience with machine vision platforms (Cognex, Keyence, Koh Young, or equivalent)- Industrial networking (Ether.Net/ IP, Profinet, OPC-UA)- Experience designing controls architecture for multi-site standardization. Preferred Qualifications- AI/ ML-based defect classification and computer vision model development- 4 years PLC/ SCADA integration experience- Experience with precision measurement and inspection devices (optical inspection, force sensors, automated gauging)- MES integration experience (SAP MES, i. Factory, or equivalent manufacturing execution systems)- SPC implementation and statistical process control methods- High-complexity electronics or precision hardware manufacturing- Experience defining controls standards that replicate across multiple facilities without re-engineering.