Forward Deployed Engineer, Energy

In this position...

While we leverage industry-standard platforms for our core ERP, CRM, and MES capabilities, our hardest and high-value problems live at the edge — where customers, operations, physical assets, and data meet software. These are not generic applications for generic business functions. They are custom-built solutions to real operational problems for real customers, built where buying a platform doesn't make sense and where we can move faster and smarter by building ourselves, accelerated by AI.

We are seeking Forward Deployed Engineers to sit at the intersection of engineering and business impact. This is a hands‑on individual contributor role for engineers who want to own outcomes, not tickets. Engineers will be directly embedded with internal stakeholders and external customers to understand real operational problems, then designing, building and deploying the software that solves them. You'll work across the full stack: application development, cloud infrastructure on GCP, enterprise data platform for AI scale, integrations with manufacturing and warehousing technology, and automation and tooling that take an idea to a production deploying.

This role is built for engineers who thrive in ambiguity, want direct exposure to the business and customer problems they're solving, and want outsized ownership and accountability over what they ship. You will not be handed a fully specified spec, instead you will be in the room helping define the problem, and then you'll build and deploy the answer.

We are building Ford Energy's engineering organization to be AI‑native from day one, not an organization that bolts AI onto existing process, but one where intelligent automation, orchestration and applied AI/ML are simply how work gets done.

This is an onsite role reporting to the Software Engineering Manager.

• Build an AI-Native Organization:
  Help define what “AI-native” actually means for Ford Energy engineering, not as a slogan but as daily practice. Using generative AI tools and AI-assisted development across the SDLC (design, implementation, testing, infrastructure, delivery), building practical AI/ML capabilities and intelligent automation that solve real operational problems, and setting the example and standards that shape how the team works as it scales.
  
  Every use of AI should tie to a clear, measurable case for the value it creates versus its cost; we use AI because it wins on merit, not because it's expected.
• Embed and Solve:
  Work directly with internal operations, product, and where applicable, customer teams to understand real business and operational problems firsthand, rather than working from a handed-down spec. Translate what you learn into shipped software.
• Own Delivery End to End:
  Wear multiple hats across the stack: writing application code (Go and related services), designing and operating cloud infrastructure on GCP, and owning the automation, environments, and pipelines that reliably get solutions into production. Success means a deployed solution driving a real outcome, not a handoff between specialized roles.
• Build the Custom Edge:
  Design, develop, and secure applications, API integrations, and edge systems that fill the gaps around our core ERP, CRM, and MES platforms. Engineer tools purpose-built for Ford Energy's business model and customers rather than generic off-the-shelf capability.
• Architect and Build, Fast:
  Move fluidly between architecture and hands‑on implementation: systems design, API integrations, cloud‑native services, IoT/edge workflows, infrastructure‑as‑code, and production‑ready software with a bias toward simple, secure, and reliable solutions deployed quickly.
• Turn Ambiguity into Deployed Software:
  Convert evolving, sometimes…