Vice President, Technology & Engineering Strategy; Infrastructure

Overview

Summary
Executive leader responsible for defining and executing Jabil’s Intelligent Infrastructure (II) technology and engineering strategy
, driving the shift to AI-enabled, platform-based engineering and infrastructure systems across design, manufacturing, and digital operations. Owns alignment of AI, data, electrical systems, and engineering workflows to deliver scalable capabilities, improved productivity, and differentiated customer solutions.

• Strategy & Roadmap
  Define enterprise II technology strategy and 3-year roadmap
• Align engineering, AI, and infrastructure investments to business outcomes
• Translate market/customer needs into scalable engineering capabilities
• Platform & Architecture Leadership
  Establish platform-first architecture across:
  • AI infrastructure (compute, networking, storage)
  • Electrical systems (power, thermal, rack integration)
  • Engineering tool chains (EDA, PLM, AI-assisted workflows)
• Define standards, reference architectures, and integration models
• AI-Enabled Engineering Transformation
  Drive adoption of AI across engineering workflows, including:
  • Design automation (schematic/PCB generation, DFM validation)
  • Planning and estimation automation
  • AI-assisted validation and design reviews
• Shift engineering from manual processes → AI-augmented execution
• Data & Digital Foundations
  Establish core data architecture (models, pipelines, reporting)
• Enable AI-ready engineering data environment
• Standardize data flows across engineering, manufacturing, and operations
• Governance & Investment
  Lead technology and architecture governance
• Drive tool rationalization and platform standardization
• Cross-Functional Execution
  Align Engineering, Digital Ops, AI PMO, Supply Chain, and BUs
• Lead global teams of architects, engineers, and technology leaders
• Ensure consistent execution across regions and programs

• Architecture & Platform
  Enterprise AI Infrastructure Reference Architectures (rack, compute, networking, power, cooling)
• Standard electrical system design frameworks (power distribution, thermal, signal integrity)
• Engineering platform architecture (EDA + AI + data integration)
• System-level block diagrams and integration models across II stack
• AI-Enabled Engineering Systems
  Deployed AI-assisted ECAD tools (schematic generation, PCB layout validation, DFM automation)
• AI-driven design review systems (requirements validation, architecture verification)
• Engineering copilots / assist tools integrated into workflows
• Automated planning, estimation, and capacity models
• Data & Digital Infrastructure
  Standardized engineering data models (design, BOM, validation, test)
• Scalable data pipelines for AI training and inference
• Integrated engineering metrics and reporting platforms
• Digital backbone enabling end-to-end traceability (design → build → validate)
• Engineering Process & Productivity
  Standardized design workflows and tool chains across II
• Reduced design cycle time via:
  • Automated checks (DFM, rules, validation)
  • AI-assisted design iteration
• Measurable improvements in:
  • Engineering throughput
  • Planning efficiency
  • Rework reduction
• Infrastructure Systems (Physical Deliverables)
  AI-optimized rack-level system designs (compute, storage, networking)
• Integrated power and cooling architectures for AI workloads
• Validated end-to-end II solutions (design → manufacturing → deployment)
• Scalable system configurations for customer-specific AI platforms
• Governance & Standards
  Published architecture standards and design guardrails
• Technology selection frameworks and tool evaluation models
• Defined AI/engineering lifecycle governance processes
• Centralized decision logs and architecture review outputs

• % adoption of standardized platforms and tool chains
• Reduction in engineering cycle time and rework
• Increase in engineering capacity without headcount growth
• Scaled deployment of AI-enabled capabilities across II
• Improved cost, performance, and time-to-market of solutions

• Bachelor’s degree in Engineering or related field
• 20+ years in engineering, systems architecture, or technology leadership
• Deep expertise in:
  • Electrical systems and infrastructure (power, thermal, compute)
  • Engineering design ecosystems (EDA, PLM, validation)
  • AI-enabled systems and digital platforms
• Proven track record driving enterprise-scale transformation

This role is central to enabling Intelligent Infrastructure by turning AI + data + engineering into scalable capability and unlocking engineering capacity and speed at scale.

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