Agentic Forward Deployed Engineer

Booz Allen Hamilton•McLean, VA

4d•$99,000 - $225,000•Remote

About The Position

As an AI‑forward engineer, you know that access to frontier models is increasingly universal, what differentiates organizations is their ability to deploy them effectively. Your strength is embedding with teams, mapping how work actually happens, and converting that understanding into reliable AI agents that transform operations. We’re seeking technical depth, advisory instincts, and an ownership mindset to bring agentic AI to workflows where it most matters. You’ll join a forward‑deployed engineering cohort that operates close to the mission, engaging on billable client programs where people execute well‑defined processes by hand. Your mission is to move from humans executing flowcharts to agents executing flowcharts and elevating humans into supervisors of that automation. You’ll map workflows and exception paths, decide where deterministic software ends and model judgment begins, and build agents that are reliable, observable, and auditable from day one. You’ll validate performance with evaluation suites and scale from shadow mode to production. Equally important, you’ll bring people along winning trust, managing change, training the workforce, and leaving every engagement more capable than you found it. You won’t do this alone. You’ll have structured reach‑back to a central engineering community focused on removing blockers, accelerating data and policy access, and turning field‑built solutions into reusable patterns, playbooks, and shared capabilities. Patterns proven once become capabilities many teams can reuse.

Requirements

8+ years of experience in software engineering, machine learning engineering, technical advising, or solutions engineering roles
4+ years of experience building applications with generative and agentic AI technologies such as LLMs, retrieval‑augmented generation, or multi‑agent orchestration
Experience deploying LLM‑powered systems or AI agents to production, including evaluation, monitoring, and iteration after launch
Experience developing production code in Python or TypeScript
Experience integrating with enterprise systems and data sources such as ERP, CRM, ITSM, or knowledge repositories
Experience working directly with customers or business stakeholders to elicit requirements, map business processes, and guide adoption of new technology
Knowledge of evaluation techniques for non‑deterministic systems, including golden datasets, regression testing, and human‑in‑the‑loop feedback
Ability to travel up to 10% of the time
Secret clearance
Bachelor’s degree

Nice To Haves

Experience working in a DoW or DoA environment
Experience with agent development and orchestration frameworks such as graph‑based planners, tool‑calling agents, or agent SDKs
Experience in forward‑deployed, embedded, or residency‑style engineering roles at client sites
Experience with organizational change management, including training, communications, workforce transition, or user adoption programs
Experience with business process analysis or process mapping such as BPMN or value‑stream mapping
Experience with major cloud platforms and containerized deployment such as public cloud services, Kubernetes, or infrastructure‑as‑code
Ability to quantify automation impact including cost savings, risk mitigation, and revenue uplift
Master’s degree
Agile or cloud Certification

Responsibilities

Embed with client teams to observe and document how work actually happens, including workflows, systems, data flows, decision points, and the exception paths that aren’t captured in formal documentation.
Produce operating maps and automation roadmaps that prioritize high‑volume workflows by expected value and risk and determine where deterministic software, agent judgment, and human‑in‑the‑loop approvals each belong.
Design, build, and deploy AI agents that reason, plan, and act across existing tools, APIs, and data sources while integrating with current systems rather than forcing migrations.
Develop evaluation frameworks, golden datasets, and regression tests to turn non‑deterministic behavior into evidence and scale agents from shadow mode to increasing autonomy to production.
Instrument everything including audit trails, monitoring, and metrics that let leadership see exactly what agents are doing and the value delivered in hours saved, risk reduced, and outcomes improved.
Drive adoption and change management to train the workforce, redesign roles around supervision of automation, and de‑risk the transformation for people living through it.
Codify learnings into playbooks, reusable components, and field feedback to continuously improve shared capabilities across engagements.
Work with us to solve real‑world challenges and define the AI and ML strategy for Army enterprise clients.