Peraton is seeking an AI Application Developer to design and build production-grade AI systems and lead the next evolution of software delivery across Government (Federal, State, and Local) 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 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.

Location: Candidate must be local to the Columbus, Ohio area.

What You’ll Do

• Architect and implement AI-enabled solutions 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

Critical Skills: AI Orchestration & Systems Thinking

LLM & Agentic Workflow Development

• 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
• Prompt engineering, prompt chaining, and reusable prompt architectures
• Evaluation frameworks for output quality, reliability, and drift

Data & Retrieval Strategy

• 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

Model & Platform Orchestration

• 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 Systems & Evaluation

• Develop scalable prompt frameworks (templates, chaining, reuse)
• Implement evaluation pipelines to measure output quality, drift, and reliability
• Ensure outputs are traceable, testable, and auditable

AI-Enabled DevSecOps, SDLC & AIOps

• 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)

Observability, Metrics & Governance

• 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

What Success Looks Like

• 20-40% improvements in in SDLC cycle time through AI-enabled workflows
• Deliver production-grade AI applications and agentic workflows deployed in secure environments
• Improve code quality, operational efficiency, and system resilience using AI
• Standardized, reusable AI orchestration patterns deployed across programs
• Measurable improvements in SLA performance, cost efficiency, and mission delivery speed