AI/ML Engineer

Booz Allen Hamilton

. Embed with client teams to observe and document workflows, systems, data flows, decision points, and exception paths .

Posted 9/23/2026full-timeUnited StatesSeniorLead💰 $99,000 - $225,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying AI agents, integrating them with enterprise systems, and driving organizational change management. Proficient in evaluating non-deterministic systems and quantifying automation impact in fast-paced environments.

Highest-signal resume keywords

AI Agent DevelopmentGenerative AI TechnologiesProduction Coding in PythonBusiness Process AnalysisOrganizational Change Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringMachine Learning EngineeringApplication DevelopmentEvaluation TechniquesRegression TestingProcess MappingAutomation Impact QuantificationAgent Orchestration FrameworksCloud DeploymentData Integration

Soft Skills

Communication with StakeholdersIndependent Operation in AmbiguityTraining and Workforce Transition

Tools & Technologies

LLMsAPIsERP SystemsCRM SystemsKubernetesInfrastructure-as-CodeBPMNValue-Stream Mapping

Certifications & Qualifications

Agile CertificationsCloud Certifications

Industry Keywords

DoW EnvironmentDoA EnvironmentChange ManagementAutomation SupervisionShadow Mode

Tech Stack

Tools & technologies

CloudERPITSMKubernetesPythonTypeScript

About the role

Key responsibilities & impact

Embed with client teams to observe and document workflows, systems, data flows, decision points, and exception paths
Produce operating maps and automation roadmaps prioritizing high-volume workflows by expected value and risk
Determine where deterministic software, agent judgment, and human-in-the-loop approvals belong
Design, build, and deploy AI agents across existing tools, APIs, and data sources
Develop evaluation frameworks, golden datasets, and regression tests
Scale agents from shadow mode to increasing autonomy and production
Instrument audit trails, monitoring, and metrics to measure agent activity and business value
Drive adoption and change management, train the workforce, and redesign roles around automation supervision
Codify learnings into playbooks, reusable components, and shared capabilities
Collaborate with a central engineering community to remove blockers and turn field-built solutions into reusable patterns

Requirements

What you’ll need

8+ years of experience in software engineering, machine learning engineering, technical consulting, 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 post-launch iteration
Experience integrating AI systems with enterprise systems and data sources such as ERP, CRM, ITSM, or knowledge repositories
Production coding experience in Python or TypeScript
Experience working directly with customers or business stakeholders to elicit requirements, map business processes, and guide technology adoption
Knowledge of evaluation techniques for non-deterministic systems, including golden datasets, regression testing, and human-in-the-loop feedback
Ability to operate independently in ambiguous, fast-moving environments and communicate with engineers and executives
Ability to travel up to 10% of the time
Secret clearance required
Bachelor's degree
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, including 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, or revenue uplift
Master's degree
Agile or Cloud Certifications

Benefits

Comp & perks

Health benefits
Life insurance
Disability benefits
Financial benefits
Retirement benefits
Paid leave
Professional development
Tuition assistance
Work-life programs
Dependent care
Recognition awards program
Identity verification and fraud prevention process
Camera-on expectation during interviews, assessments, and virtual meetings