Senior AI Engineer

Peraton•Reston, VA

23d•$112,000 - $179,000•Hybrid

About The Position

Peraton is seeking a Senior AI Engineer to design and build production-grade AI systems and lead the next evolution of software delivery across Defense & Health programs by operationalizing AI at scale. This role is focused on embedding AI across the Software Development Life Cycle (SDLC) focused on LLM integration, agent-based systems, and AI-native software engineering, DevSecOps with AI —transforming how systems are built, tested, secured, and operated via AI driven development. You will design and implement AI-orchestrated, agent-driven workflows leveraging cloud-native platforms and secure government AI environments (including GenAI.mil). The objective is to move beyond isolated AI use cases and deliver repeatable, governed, and measurable AI-enabled systems that accelerate delivery of to scalable, mission-ready AI solutions. This is an engineer role for someone who understands that real impact comes from orchestrating models, data, and workflows into production-grade capabilities. This position will report to Reston, VA with occasional telework options.

Requirements

US Citizenship
Active Secret clearance
5 years with BS/BA
5–10+ years of experience in software engineering, DevSecOps, platform engineering, or related field
2+ years of hands-on experience building AI/LLM-based applications or workflows
Demonstrated experience integrating AI/LLM-based capabilities into engineering or operational workflows
Experience with LLM frameworks and orchestration tools (e.g., LangChain, LlamaIndex, AutoGen, CrewAI, or similar)
Strong expertise in cloud-native architectures (AWS, Azure, or GCP)
Deep understanding of CI/CD pipelines, DevSecOps practices, and modern SDLC frameworks
Strong program skills in Python and at least one additional language (Java, JavaScript, Go, etc.)
Experience designing and deploying distributed systems, APIs, and microservices-based architectures
Build and optimize RAG architectures and secure data access patterns
Structure and govern data (codebases, runbooks, tickets, documentation) for effective AI consumption
Design, build and maintain Vector databases and semantic search
Ensure data lineage, integrity, secure access patterns and classification compliance
Orchestrate across multiple models and endpoints, including GenAI.mil
Implement routing, fallback, and optimization strategies based on latency, cost, and accuracy
Design for secure, compliant AI usage in federal environments
Prompt engineering, prompt chaining, and reusable prompt architectures
Evaluation frameworks for output quality, reliability, and drift

Nice To Haves

Direct experience with GenAI.mil or other secure government AI platforms
Expertise in agent frameworks, LLM orchestration, or emerging AI workflow tooling
Experience with Kubernetes, containerized environments, and platform engineering
Familiarity with MLOps, AIOps, or AI governance frameworks
Experience supporting DoD, DHA, or federal health systems (e.g., MHS GENESIS)
Experience deploying AI solutions in IL4/IL5 or FedRAMP High environments
Active TS/SCI clearance

Responsibilities

Architect and implement AI-enabled DevSecOps pipelines that accelerate code generation, testing, security, documentation, and deployment
Design and build LLM-powered applications and agentic systems for software development, testing, security, and operations
Design and operationalize agentic, multi-step workflows (e.g., code → test → validate → deploy) with appropriate human-in-the-loop controls
Leverage and integrate GenAI.mil models and commercial LLMs with cloud-native AI services into secure, scalable development environments
Build and integrate AI microservices and APIs into cloud-native platforms
Build future-state architecture and data pipelines that ground AI outputs in authoritative, mission-relevant data
Establish prompt frameworks, chaining strategies and reusable AI patterns that scale across teams and programs
Integrate AI into IT operations (ticket triage, root cause analysis, observability, incident response) to enable closed-loop automation
Define and track performance metrics (cycle time, defect reduction, cost-per-feature, SLA improvements) tied to AI adoption
Lead technical adoption across teams, mentoring engineers and standardizing best practices
Ensure compliance with federal security, data governance, and AI usage policies
Implement RAG architectures using mission data (codebases, documentation, operational data) to ground AI outputs
Design and implement multi-agent orchestration, tool integration and workflow automation with tool use, memory, and feedback loops
Balance automation, control, and reliability in mission-critical environments
Develop scalable prompt frameworks (templates, chaining, reuse)
Implement evaluation pipelines to measure output quality, drift, and reliability
Ensure outputs are traceable, testable, and auditable
Embed AI into CI/CD, security scanning, testing, and documentation workflows
Apply AI to operations (incident response, anomaly detection, automated remediation)
Enable closed-loop systems (detect → decide → act)
AI-assisted SDLC development workflows and pipeline integration (code, test, security, documentation)
Define KPIs tied to AI-driven performance gains
Implement monitoring for AI system behavior, cost, and outcomes
Align with DoD/DHA governance, security, and compliance frameworks