Forward Deployed Engineer

ModSquad•AL Remote, MS

56d•Remote

About The Position

The Forward Deployed Engineer operates where the standard implementation playbook breaks and they don't just fix it, they write the next one. You embed directly with clients to take complex AI systems from struggling pilot to production-grade capability. Your engagements involve custom agentic architectures, legacy systems that weren't designed for this, change-resistant organizations, and real stakes. This is part engineering, part consulting, part product management. You live in the gap between "this should be possible" and "this is running in production." Most people find that gap uncomfortable. You find it interesting.

Requirements

5–7+ years in technical implementation, solutions engineering, or consulting — with significant experience in complex, high-stakes client environments.
Proven experience building and deploying agentic AI systems in production — not pilots or proofs of concept, but things that ran, were used, and were maintained.
Deep fluency with AI deployment patterns — prompt engineering, agent orchestration, evaluation frameworks, observability, and guardrails.
Strong integration and API skills — you've worked in messy, undocumented environments and made things work anyway.
The consulting instinct to find the real problem before solving the stated one — and the credibility to tell a senior client when they're solving the wrong problem.
Outstanding communication skills — you can run a working session with front-line staff in the morning and present deployment architecture to a CTO in the afternoon.
Experience shipping something inside a large organization and staying to see it operate — you understand the last mile of deployment, not just the build.
Comfort with ambiguity — you make sound calls without a complete picture and document what you learn when you get one.
High EQ, personal accountability, and a sense of humor.
You thrive in ambiguous, high-stakes, fast-moving environments.

Responsibilities

Embed with a small portfolio of high-complexity client accounts — designing, building, and deploying AI systems that work in their real environment, not a demo.
Run deep discovery: map actual workflows, identify real friction, and understand the organizational and technical landscape before a single line of configuration is written.
Build production-grade agentic systems — multi-step AI workflows, tool-calling architectures, evaluation frameworks, and the human-in-the-loop structures that keep them governed.
Navigate real complexity: legacy systems, undocumented infrastructure, compliance requirements, and change management challenges — the things that stall lesser engagements.
Build deployment playbooks from your engagements — documenting what good looks like so every subsequent deployment starts from a higher floor.
Partner with the broader Solutions Engineering team to bring field intelligence back to architectural standards and tooling choices.
Travel to client sites as the work requires — this role may involve meaningful in-person time with clients.