AI/ML Platform & Agentic Automation Engineer

Jobverse.io; Chantilly (VA); On-site; USD 130,000 - 170,000; Full time

Job summary

Peraton is seeking an AI/ML Platform & Agentic Automation Engineer to integrate approved AI models and managed services into enterprise platforms via reusable APIs and agentic workflows. The role involves building developer assistants, self-service automation, and auditable operations across cloud and restricted environments.

Responsibilities include integrating foundation models, creating service wrappers, enabling model updates with minimal code changes, and connecting AI tools to

Responsibilities

• Integrate approved foundation models and managed AI services 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
• 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
• 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
• 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
• 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 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

Job description

Peraton is hiring an AI/ML Platform & Agentic Automation Engineer to integrate approved AI models and managed services into enterprise platforms through reusable APIs and agentic workflows. The role also develops developer-assistance and self-service automation while supporting secure, auditable operations across cloud and restricted environments.

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 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
• 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
• 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
• 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
• 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 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