Forward Deployed Engineer

Forward Deployed Engineer, alliant AI Services

alliant is building an engineering-led delivery model for its AI practice, and we're looking for a Forward Deployed Engineer (FDE) to embed directly with our clients: running technical discovery, architecting solutions, writing code, and owning the ROI case alongside the build. Unlike an FDE at a product company, you will not be adapting a fixed platform to a customer's use case.

You will combine alliant's reusable AI accelerators, reference architectures, agent templates, prompt libraries, and industry playbooks into solutions built for each client's environment. The goal is to adapt proven IP quickly and return what you learn to the shared library, so the next engagement moves faster and delivers at higher quality. This is a hands‑on role in a practice that's intentionally still being shaped.

You are part software developer, part value‑engineer, part product thinker; and your success is measured as much by the ROI and impact recognized by a client as by the code you ship. Technologists here need to build real commercial fluency and stakeholder range; delivery, BA, and PM professionals need to build hands‑on skills in AI‑assisted development, solution design, and rapid prototyping.

Wherever you start, you're expected to grow across that full spectrum. You'll work alongside PMs, solution architects, software engineers, and data scientists in a model built for close collaboration rather than functional silos. As a premier consulting and technology firm, alliant is focused on providing solutions to help businesses transform and thrive, while serving as their ongoing growth partner.

• Diagnose before you build. Embed with client stakeholders to understand their technical environment, workflows, and business goals, as well as quantify the value case (cost savings, cycle-time reduction, revenue impact) before a line of code gets written.
• Build from the accelerator library, not from scratch. Configure and extend alliant's reusable agent patterns, reference architectures, and prompt libraries into AI-driven integrations and agentic workflows tailored to each client; favoring speed-to-value over bespoke engineering