Senior AI Platform Engineer

Primary Responsibilities

• Lead the technical accreditation strategy for the agentic AI platform, including security controls for models, tools, and agent-to-agent interactions.
• Develop and maintain technical artifacts supporting ATO and continuous ATO (cATO), including control narratives, architecture diagrams, data flows, boundary definitions, POA&M inputs, and security scan evidence.
• Partner with security engineering, ISSO/ISSM personnel, and AO stakeholders to translate complex AI and cloud architectures into evidence supporting authorization decisions.
• Design, build, and operate a secure AWS GovCloud IL5 platform supporting agentic AI workloads.
• Deploy and manage containerized workloads using Kubernetes and Docker, including scaling, observability, resiliency, and failure recovery.
• Build and maintain CI/CD pipelines and Infrastructure as Code (IaC) using Platform One and technologies such as Terraform or CloudFormation.
• Support container hardening, image accreditation, and secure software delivery practices.
• Integrate Amazon Bedrock and other authorized AI/ML services, managing IAM, network boundaries, service quotas, and data flow controls.
• Implement logging, tracing, monitoring, auditability, and cost attribution for agent and tool activity.
• Apply MLOps practices including model and prompt versioning, deployment gating, monitoring, and rollback.
• Mentor engineers on secure, accreditable architecture and development practices.
• Develop and execute the platform technical roadmap aligned with program and mission objectives.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field.
• 8+ years of hands-on experience in cloud, platform, DevOps, or MLOps engineering.
• Demonstrated experience supporting a system through formal security accreditation/RMF, including substantial contributions to security control documentation and direct interaction with security or authorization stakeholders.
• Experience operating within a DoD or federal accredited cloud environment, such as IL4/IL5, FedRAMP Moderate/High, or comparable.
• Hands-on experience with AWS, Kubernetes, Docker, and Infrastructure as Code.
• Experience designing, building, and maintaining CI/CD pipelines.
• Proficiency in Python.
• Working knowledge of how ML/LLM workloads are deployed, scaled, secured, and monitored.
• Strong communication skills with the ability to explain and defend technical architecture and security decisions.

Preferred Qualifications

• Active TS/SCI clearance.
• Experience supporting national security, defense, or DoD environments.
• Direct experience with continuous ATO (cATO), continuous monitoring, and pipeline-based security evidence.
• Experience with Platform One, Iron Bank, or Big Bang.
• Experience with AWS Bedrock, Bedrock AgentCore, SageMaker, or similar managed AI/ML services.
• Experience securing or accrediting systems involving LLMs, autonomous decision logic, agentic AI, or dynamic service interactions.
• Experience with cybersecurity operations, security engineering, or DoD cyber environments.
• DoD 8570 IAT Level II certification or equivalent.

Original Posting: September 17, 2026

For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

Pay Range $107,900.00 - $195,050.00

The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.