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 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.
Qualifications
Required Qualifications
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.
Desired Qualifications
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.
Target Salary Range $112,000 - $179,000. This represents the typical salary range for this position. Salary is determined by various factors, including but not limited to, the scope and responsibilities of the position, the individual’s experience, education, knowledge, skills, and competencies, as well as geographic location and business and contract considerations. Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay.
Benefits Statement
Peraton offers eligible employees a variety of benefits including medical, dental, vision, life, health savings account, short/long term disability, EAP, parental leave, 401(k), paid time off (PTO) for vacation, and company paid holidays. A full listing of available benefits can be viewed at https://www.careers.peraton.com/benefits.
Application Statements The application period for the job is estimated to be 30 days from the job posting date. However, this timeline may be shortened or extended depending on business needs and the availability of qualified candidates. By applying to this job, you are expressing interest in the role and the Company. During the review of your application, you may be required to participate in an on-camera interview, as well as participate in a process to verify your identity. Use of artificial intelligence (AI) tools of any kind during Peraton interviews is strictly prohibited unless the candidate has obtained prior written authorization. All interview responses must be the candidate’s own.
- EEO
- EEO: Equal opportunity employer, including disability and protected veterans, or other characteristics protected by law.
