AI/ML Platform & Agentic Automation Engineer

About Peraton

Peraton is a next-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. As the world's leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies. Peraton operates at the critical nexus between traditional and nontraditional threats across all domains: land, sea, space, air, and cyberspace. The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces. Each day, our employees solve the most daunting challenges that our customers face. Visit peraton.com to learn how we're keeping people around the world safe and secure.

About the role

We are seeking an AI/ML Platform & Agentic Automation Engineer to design, develop, integrate, and maintain shared AI capabilities and intelligent workflow automation across enterprise platforms. This role focuses on integrating approved foundation models and managed AI services through reusable APIs, agentic workflows, and developer-facing tools rather than developing foundation models from scratch. The ideal candidate combines strong software engineering and cloud expertise with experience integrating AI services into secure, automated workflows. The engineer will collaborate with platform, DevSecOps, identity, cybersecurity, and operations teams to deliver reliable, auditable, and secure AI-enabled capabilities.

Responsibilities

• AI/ML Platform and Model-Service Integration
  • Integrate approved foundation models and managed AI services, including AWS Bedrock or comparable platforms, into enterprise applications and workflows.
  • Develop reusable APIs, service wrappers, and integration patterns that enable consistent access to model capabilities.
  • Implement configurable model-selection and integration approaches that support model updates without requiring extensive changes to consuming applications.
  • Support deployment and integration within cloud and restricted environments where approved AI services are available.
• Agentic Workflow Engineering
  • Design and implement agentic workflows to automate repeatable platform engineering, service management, and operational processes.
  • Integrate AI agents with approved tools and APIs, including IT service management platforms, source control systems, infrastructure automation, knowledge repositories, and observability services.
  • Establish appropriate workflow boundaries, approval requirements, tool permissions, auditability, and failure-handling mechanisms.
  • Develop reusable automation components for account provisioning, access requests, environment deployment, incident enrichment, technical documentation retrieval, and other approved use cases.
• Developer Assistance and Self-Service Automation
  • Develop and maintain AI-powered developer assistants, chat interfaces, and low-code/no-code capabilities.
  • Connect developer-facing tools to approved technical documentation, platform guidance, service catalogs, code examples, and engineering resources.
  • Integrate AI assistance into development workflows while protecting credentials, sensitive information, and privileged platform functions.
  • Collaborate with platform and developer-support teams to identify repetitive requests that can be addressed through self-service tools and guided automation.
• Security and Enterprise Platform Integration
  • Integrate AI services with identity and access management, role- and attribute-based access controls, service identities, secrets management, and audit mechanisms.
  • Enforce established security boundaries, access policies, and tenant isolation requirements across AI services and agent workflows.
  • Coordinate with cybersecurity teams to implement appropriate guardrails, data-handling controls, logging, and authorization requirements.
  • Instrument AI services and automated workflows to support monitoring of availability, usage, failures, latency, and performance.
• Software Engineering and Lifecycle Support
  • Maintain AI platform components, workflow definitions, prompts, tool integrations, and configuration through source control and established engineering review processes.
  • Develop automated tests and evaluation scenarios to validate critical workflows before deployment to operational environments.
  • Troubleshoot model-service integrations, agent