AI Factory Customer Engineer

About the Role

The AI Factory Customer Engineer plays a pivotal role in bridging the gap between customers and Armada's Product and Engineering teams. This role requires deep technical credibility, hands-on infrastructure experience, and strong interpersonal skills to translate complex AI infrastructure and data center architectures into clear, practical solutions for customers.

This role goes beyond traditional pre-sales. The Customer Engineer acts as the technical interface between customers and Armada Engineering, ensuring customer requirements, constraints, and feedback are accurately represented and incorporated into solution design and future platform evolution.

Armada's AI Factory serves a broad and growing set of customers across enterprise, industrial, and infrastructure-heavy verticals. A meaningful and growing subset of strategic targets includes AI-focused datacenter co-location providers and Neocloud operators which represent a primary segment where Armada's modular, liquid-cooled AI Factory is a compelling fit for capacity expansion and rapid deployment. Additionally, renewable energy operators who own or control significant land, power, and stranded energy assets are increasingly positioned to host or operate AI compute infrastructure at scale.

The ideal candidate brings an engineering-first mindset, strong data center and infrastructure expertise, particularly around liquid-cooling, modular data centers, Nvidia architecture, and the energy, curiosity, and positivity required to operate in fast-moving, ambiguous environments. This role is instrumental in driving adoption, trust, and long-term success for Armada's AI Factory offerings.

What You'll Do — Key Responsibilities

Core Technical Customer Engagement

• Act as the primary technical interface between customers and Armada's Product and Engineering teams, translating real-world requirements into actionable technical inputs.
• Provide expert technical guidance on Armada's AI Factory products, including Modular Data Centers (MDCs), liquid-cooled data center designs, and GPU-based systems from partners such as NVIDIA.
• Understand customer workloads deeply both current and future and advise on rack-level design, system architecture, and deployment tradeoffs.
• Lead technical demonstrations, proof-of-concepts, and workshops, tailoring content to customer constraints and operational realities.
• Cultivate trusted-advisor relationships with customer engineering, infrastructure, IT, OT, and security stakeholders.
• Enable field and sales teams by simplifying complex technical concepts into clear, customer-relevant narratives.
• Bring a high-ownership, self-starting mindset with the energy and curiosity to fill gaps, solve problems, and drive outcomes end-to-end.

Renewable Energy & Land-and-Power Operator Engagement

• Engage renewable energy operators, independent power producers, and upstream oil and gas companies (e.g., Chord Energy, Devon Energy, and similar) as a strategic and growing customer segment for Armada's AI Factory — helping them understand how their existing power generation capacity, land assets, and fiber infrastructure make them natural hosts for modular AI compute deployments.
• Articulate the value proposition of Armada's MDC platform to energy sector operators: rapid deployment timelines, self-contained power and cooling architecture, scalable capacity, and the ability to monetize stranded or excess power assets through AI compute hosting.
• Develop familiarity with the operational model of energy companies: power generation economics, land and lease structures, regulatory constraints, and the OT/IT boundary — and translate these into informed AI Factory deployment conversations.
• Navigate both the OT and IT stakeholder landscape within energy companies, building credibility with infrastructure, operations, and executive teams alike.
• Identify and communicate the specific deployment considerations relevant to energy sector environments: remote or off-grid locations, variable power availability, environmental and permitting requirements, and physical security constraints.

AI Datacenter Co-Lo & Neocloud Engagement

• Engage AI-focused datacenter co-location operators and Neocloud providers as a secondary but relevant customer segment, positioning Armada's AI Factory as a solution for rapid capacity expansion, modular overflow deployments, and high-density liquid-cooled compute environments.
• Develop sufficient familiarity with co-lo and Neocloud operational models — GPU cluster economics, high-density power and cooling requirements, multi-tenant compute, and deployment velocity expectations — to hold credible technical conversations and scope solutions appropriately.
• Collaborate with Sales to identify where AI Factory's modular, rapidly deployable infrastructure is most differentiated against co-lo and Neocloud alternatives, and tailor technical positioning accordingly.

Cross-Functional Collaboration & Field Strategy

• Collaborate cross-functionally with Sales, Product, and Engineering to support customer engagements, technical scoping, and solution design across all target verticals.
• Stay current on industry trends related to AI infrastructure, energy sector AI adoption, data center design, liquid cooling, and modular systems — and share insights internally to shape future product strategy.
• Bring positive energy, curiosity, and a collaborative mindset to every engagement — this role values technical excellence and having fun while building something meaningful.

Required Qualifications

• Bachelor's degree in Engineering, Computer Science, or a related field (or equivalent hands-on engineering experience). Advanced engineering degrees are a plus.
• 5+ years of relevant experience in data center engineering, infrastructure engineering, pre-sales, sales engineering, or solution architecture roles.
• Years of experience are less important than depth and relevance to data center and infrastructure topics.
• Strong understanding of compute, networking, and storage fundamentals, including AI and GPU-based systems (e.g., NVIDIA DGX, HGX, MGX, or similar).
• Hands-on experience with data center systems, including:

• Skills & Tags

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