Agentic AI Forward-Deployed Engineer

Booz Allen Hamilton

Confirmed live today High trust

Quick summary

Work type
On-site
Location
McLean, VAArlington, VA
Salary
$99,000–$225,000 / yr
Employment
Full-time
Posted
8 days ago
Freshness
Confirmed live today

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $204k
This role $162k
$82k most similar roles pay here $259k

This role pays less than 77% of similar roles. Most pay $167,125–$240,000 — the shaded band above. At the midpoint, this role pays about $162k versus about $204k for comparable roles.

Based on 240 similar postings.

Employer

About Booz Allen Hamilton

Booz Allen Hamilton is a management and technology consulting firm that provides analytics, digital, engineering, and cybersecurity solutions primarily to U.S. government agencies and commercial clients. Industry: Management & Technology Consulting

Booz Allen Hamilton currently has 1159 open roles on FindRole.

Listed pay typically runs $86,800–$198,000 across 804 roles with salary data.

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At a glance

TL;DR · Agentic AI Forward-Deployed Engineer

As an Agentic AI Forward-Deployed Engineer within the Global Defense Disruption Cell, you will embed with program teams to transform manual workflows into reliable, observable, and auditable AI agents. You will be responsible for mapping complex business processes, identifying exception paths, and determining where deterministic software ends and model judgment begins. Your daily work involves building agentic systems that reason and act across existing tools and APIs while developing evaluation frameworks, golden datasets, and regression tests to ensure production readiness. To succeed, you must possess expertise in Python or TypeScript, along with experience in LLMs, RAG, MCP, or multi-agent orchestration. You will also manage change management efforts, training workforces to supervise automation. The role focuses on solving the challenge of deploying agentic AI into high-volume workflows within defense programs to improve efficiency and reduce risk.

What you'll do

  • Map and document complex workflows, data flows, and exception paths by embedding with program teams.
  • Create automation roadmaps that prioritize high-volume tasks based on risk and expected value.
  • Design and deploy AI agents that reason and act across existing tools, APIs, and data sources.
  • Develop evaluation frameworks and golden datasets to validate non-deterministic behavior before production scaling.
  • Instrument agent actions with audit trails and metrics to demonstrate measurable value to leadership.
  • Lead change management by training the workforce and redesigning roles around automation supervision.
  • Codify field findings into reusable playbooks and components for the broader engineering team.

What we're looking for

  • Master's degree (preferred).
  • 5+ years of experience in software engineering, machine learning engineering, technical advisory, or solutions engineering roles.
  • 2+ years of experience building applications with generative and agentic AI technologies, including LLMs, RAG, MCP, or multi-agent orchestration.
  • Experience deploying an LLM-powered system or AI agent into a production environment, including evaluation, monitoring, and iteration after launch.
  • Experience developing production code in Python or TypeScript.
  • Experience working with stakeholders to elicit requirements, map business processes, and guide adoption of new technology.
  • Knowledge of evaluation techniques for non-deterministic systems, such as golden datasets and regression testing.
  • Ability to obtain a Secret clearance (Secret clearance preferred).
  • Experience with agentic coding/orchestration tools like LangGraph or OpenAI Agents SDK (preferred).
  • Experience in forward deployed, embedded, or residency-style engineering roles at customer sites (preferred).
  • Experience with organizational change management and training programs (preferred).
  • Experience with business process analysis, including BPMN or value stream mapping (preferred).
  • Experience with cloud platforms, containerized deployment, and enterprise systems integration (preferred).

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