AI Application Developer

Responsibilities

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 s 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, Dev Sec Ops  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 Dev Sec Ops , 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…