AI Process Integration Engineer

Peraton•McLean, VA

About The Position

Peraton is seeking an AI Process Integration Engineer. This position will sit at the intersection of artificial intelligence deployment and mission workflow optimization — responsible for bridging the gap between approved, available AI/ML tools and their effective operational use across intelligence analysis, targeting, and screening and vetting workflows. This role does not wait for new tools to be approved; it maximizes the mission value of what is already on the network by redesigning the processes around those tools, configuring them for mission-specific use cases, and ensuring analysts can leverage them from Day 1.

Requirements

Active TS/SCI with Polygraph required
12 years of experience, may have supervisory or management experience
Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field.
10+ years of experience in AI/ML tool deployment, systems integration, or business process engineering; at least 5 years supporting IC, DoD, or Federal law enforcement analytic environments.
Proficiency in AI/ML tool configuration, prompt engineering, workflow modeling (BPMN), and data pipeline management; experience with IC-approved analytic platforms and multi-classification network environments.
Working knowledge of Lean Six Sigma, Agile, and continuous improvement frameworks applied to operational or intelligence environments.
Strong analytical thinking, clear written and verbal communication, and the ability to translate technical AI capability into practical mission value for non-technical analysts.

Responsibilities

Assess approved AI/ML tools currently available on the customer network and evaluate their operational readiness, configuration gaps, and underutilization.
Configure, optimize, and integrate approved tools into existing analytic and targeting workflows without introducing unapproved capabilities or triggering additional review board requirements.
Develop mission-specific use-case configurations that align tool functionality to analyst tasks — entity triage, credibility scoring, pattern correlation, document production, and RFI processing.
Maintain tool performance baselines and identify configuration adjustments that improve output accuracy, speed, and analyst adoption.
Map current-state analytic and operational workflows to identify where approved AI tools can eliminate manual bottlenecks, reduce redundant data entry, and compress cycle times.
Design optimized future-state workflows that embed AI tool touchpoints at the highest-friction points in the intelligence production and targeting cycle.
Develop before/after process documentation with measurable performance targets tied directly to mission outcomes.
Maintain SOPs and workflow guides that reflect the integrated AI-enabled process architecture.
Build mission-specific prompt libraries, Boolean-to-AI logic translation guides, and structured templates that make approved tools immediately usable by analysts without requiring technical expertise.
Develop a Document Support Playbook Suite covering draft assist, tradecraft review, source synthesis, consistency checking, and classification review workflows.
Ensure all prompt engineering products are tool-agnostic and adaptable to any customer-approved platform upgrade or replacement.
Establish KPIs tracking AI tool utilization rates, analyst productivity gains, cycle time reductions, and product quality improvements.
Provide leadership with data-driven evidence supporting review board decisions to expand AI tool access or activate additional use cases.
Apply Lean Six Sigma and continuous improvement methodologies to iteratively refine AI-integrated workflows based on operational feedback.
Work directly with analysts, targeters, mission leads, and IT teams to drive adoption of AI-integrated workflows through hands-on demonstration, embedded support, and structured enablement.
Develop transition plans and training materials that ensure smooth integration of AI tools into daily mission operations with zero workflow disruption.
Serve as the operational bridge between the technical AI/ML engineering team, the analytic workforce, and program leadership.