AI/ML Platform & Agentic Automation Engineer

Peraton•Chantilly, VA

1d•$112,000 - $179,000

About The Position

The AI/ML Platform & Agentic Automation Engineer designs, develops, integrates, and maintains shared AI capabilities and intelligent workflow automation across enterprise platforms. The role integrates approved foundation models and managed AI services through reusable APIs, agentic workflows, and developer-facing tools rather than building foundation models from scratch. It combines software engineering and cloud expertise with experience incorporating AI services into secure, automated workflows. The engineer collaborates with platform, DevSecOps, identity, cybersecurity, and operations teams to deliver reliable, auditable, and secure AI-enabled capabilities. The engineer integrates approved models and managed services into enterprise applications, develops reusable service interfaces, and supports model changes and deployments in cloud and restricted environments. The role also creates agentic workflows and reusable automation for platform and operational processes, with appropriate permissions, approvals, auditability, and failure handling. Developer-facing responsibilities include maintaining AI assistants and self-service tools connected to approved technical resources while protecting sensitive information and privileged functions. The position further integrates AI services with enterprise identity, access control, secrets management, and audit mechanisms; implements security guardrails and monitoring; maintains code and configuration through established engineering processes; develops automated tests and evaluation scenarios; troubleshoots integrations and workflows; and coordinates controlled updates to approved models and services.

Requirements

Active TS/SCI clearance with CI Polygraph.
BS/BA and 5 years of experience; an additional 4 years of experience may be considered in lieu of a degree.
Approximately 5–8 years of experience in software engineering, platform engineering, cloud engineering, workflow automation, applied AI/ML engineering, or a related technical discipline.
Hands-on experience integrating applications with foundation models or managed AI services through APIs.
Strong software development or scripting skills in Python or a comparable programming language.
Experience developing workflow automation that integrates with external tools, services, or APIs.
Experience with AWS services and cloud-native application development patterns.
Familiarity with Git-based development, CI/CD pipelines, containers, and modern software delivery practices.
Understanding of authentication, authorization, secrets management, and secure service integration.
Demonstrated troubleshooting skills across distributed applications and automated workflows.
Ability to collaborate effectively with software, platform, cybersecurity, and operations teams.

Nice To Haves

Hands-on experience with AWS Bedrock or comparable managed foundation-model services.
Familiarity with Bedrock Agents or other agent-orchestration frameworks.
Experience implementing retrieval-augmented generation (RAG) using approved enterprise data sources.
Experience integrating ServiceNow, GitLab, or comparable enterprise platforms through APIs.
Familiarity with Terraform, CloudFormation, Ansible, or other infrastructure automation tools.
Experience with OpenTelemetry or comparable application instrumentation and observability tools.
Familiarity with Kubernetes and container orchestration.
Experience working in classified, restricted, or otherwise highly regulated cloud environments.
Experience evaluating and testing large language model (LLM) applications, prompts, and agentic workflows.

Responsibilities

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 for consistent access to model capabilities.
Implement configurable model-selection and integration approaches that accommodate model updates without extensive changes to consuming applications.
Support deployment and integration in cloud and restricted environments where approved AI services are available.
Design and implement agentic workflows for 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 workflow boundaries, approval requirements, tool permissions, auditability, and failure-handling mechanisms.
Develop reusable automation for account provisioning, access requests, environment deployment, incident enrichment, technical documentation retrieval, and other approved use cases.
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 suitable for self-service tools and guided automation.
Integrate AI services with identity and access management, role- and attribute-based access controls, service identities, secrets management, and audit mechanisms.
Enforce security boundaries, access policies, and tenant-isolation requirements across AI services and agent workflows.
Coordinate with cybersecurity teams to implement guardrails, data-handling controls, logging, and authorization requirements.
Instrument AI services and automated workflows to monitor availability, usage, failures, latency, and performance.
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 operational deployment.
Troubleshoot model-service integrations, agent behavior, workflow failures, and developer-assistance capabilities.
Evaluate approved model and service updates and coordinate controlled implementation with AI/ML operations, assurance, and platform engineering teams.