AI Infrastructure Engineer - Emerging Technologies

About the Position

The AI Infrastructure Engineer – Emerging Technologies will support the Office of the VP of Technology Engineering & Innovation in evaluating, designing, and developing next‑generation AI‑ready data center infrastructure strategies.

This role serves as a bridge between emerging AI technologies and practical implementation across data center development, engineering, construction, operations, and energy infrastructure planning.

It focuses on assessing how rapidly evolving AI compute architectures, high‑density rack deployments, advanced cooling systems, and emerging power technologies will impact future data center design standards, development strategies, construction methodologies, and operational models.

The ideal candidate combines expertise in AI infrastructure, power systems, cooling technologies, and emerging compute platforms with the ability to translate industry trends into actionable engineering and infrastructure strategies for future AI‑enabled data center environments.

This role is ideal for someone who is highly analytical, technically curious, and capable of bridging emerging AI compute trends with real‑world infrastructure strategy and execution.

This individual should be comfortable operating across engineering, operations, construction, energy strategy, and innovation functions while helping shape the future direction of AI‑enabled data center development.

• Support the VP of Technology Engineering & Innovation in evaluating emerging AI infrastructure technologies and future‑ready data center strategies.
• Analyze AI workload characteristics including training vs. inference workloads, GPU utilization patterns, dynamic workload fluctuations, rack‑level power variability, and networking and latency requirements.
• Assess implications of AI workload behavior on infrastructure resiliency, scalability, efficiency, and operational design.
• Develop technical recommendations and infrastructure strategies supporting future AI deployments.

• Analyze current and future AI compute platforms including NVIDIA GPU architectures, ARM‑based platforms, custom AI accelerators and ASICs, optical networking and switching technologies, and emerging hyperscaler‑designed AI chips.
• Evaluate implications of evolving chip architectures on rack density, power consumption, cooling requirements, electrical distribution, mechanical infrastructure, space planning, and future development standards.
• Model current and future AI rack power density trends covering existing high‑density deployments (50–120 kW), near‑term AI deployments (150–300+ kW), and future ultra‑dense AI cluster scenarios.
• Assess long‑term impacts of emerging chip architectures on energy efficiency and future data center design and development standards.

• Support conceptual and detailed design efforts for AI‑ready data center infrastructure.
• Assist in developing long‑term infrastructure roadmaps for high‑density AI deployments, liquid cooling adoption, modular infrastructure strategies, utility coordination, grid‑parallel and microgrid solutions, and future AI campus development.
• Evaluate implications of AI infrastructure evolution on greenfield developments, existing facility retrofits, construction methodologies, scalability, and future campus master planning.
• Collaborate with engineering, development, and construction teams to develop scalable AI‑ready infrastructure standards and deployment models.

• Collaborate closely with the Energy Strategy Team to evaluate utility constraints, interconnection requirements, grid limitations, dynamic load fluctuation impacts, power quality and resiliency considerations, and onsite generation and distributed energy solutions.
• Support analysis of grid‑parallel and islanded microgrid architectures, fuel cells, battery energy storage systems (BESS), bridge power solutions, natural gas generation, and renewable integration opportunities.
• Evaluate implications of AI workloads on substation development, transmission planning, utility…