Senior AI Ops Engineer Security Clearance

Position: Senior AI Ops Engineer with Security Clearance
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  to learn how we're keeping people around the world safe and secure. Program Overview About

The Role U.S. Customs and Border Protection (CBP) is seeking an AI Operations (AIOps) Engineer to build and operate secure, scalable, and mission-ready AI/ML and LLM systems in production activities in support of the CBP analytics and intelligence support program. This role ensures reliable deployment, monitoring, governance, and continuous improvement of enterprise AI platforms supporting mission-critical analytics and decision support. The ideal candidate brings strong reliability engineering, AI/ML operational expertise, security and compliance awareness, cost optimization discipline, and the ability to collaborate across technical and mission teams in a high-assurance federal environment.

Support will be provided across multiple mission locations:
* Ashburn, VA
* Sterling, VA
* Washington, D.C.

Key Responsibilities AI Platform Engineering
* Design and operate cloud and on-prem AI/ML platforms supporting model training, batch scoring, real-time inference, and RAG-based LLM applications.

* Deploy containerized workloads to Kubernetes and manage high availability, autoscaling, and release strategies.

* Integrate model serving frameworks and feature stores to support scalable production inference.

CI/CD & Model Lifecycle
* Build and maintain CI/CD pipelines for code, data, and models.

* Implement model versioning, registries, promotion gates, and environment parity.

* Develop reproducible training and deployment workflows using infrastructure-as-code and orchestration tools.

Observability & Reliability
* Implement monitoring for system health, model performance, and model quality (including drift and bias indicators).

* Define and manage SLOs/SLAs and participate in incident response for AI services.

* Develop playbooks to address outages, regressions, and quality degradation.

LLMOps & Guardrails
* Operate LLM applications and RAG pipelines.

* Manage vector databases and evaluation frameworks.

* Implement AI safety controls, including prompt validation, content filtering, PII protection, and performance evaluation.

* Optimize inference efficiency and infrastructure utilization.

Security, Compliance & Governance
* Enforce authentication, authorization, encryption, and secrets management best practices.

* Implement controls supporting PII protection, audit logging, and data governance.

* Align AI operations with NIST AI RMF, ISO 27001, SOC 2, and DHS/CBP security policies.

* Support Responsible AI practices including bias testing, explainability, and human oversight.

Cost & Performance Optimization
* Monitor and optimize compute (GPU/CPU), storage, and network utilization.

* Implement autoscaling and cost-efficient infrastructure strategies.

* Provide visibility into per-model costs and capacity planning.

Collaboration & Enablement
* Partner with data scientists and ML engineers to product ionize models.

* Develop reusable templates, documentation, and operational runbooks.

* Translate mission and compliance requirements into technical platform capabilities.
** Position is contingent upon contract award
** Qualifications

Required Qualifications
* Minimum of 8 years with BS/BA;
Minimum of 6 years with MS/MA. 12 years with a HS diploma/equivalent can be considered in lieu of a degree.

* 5+ years of experience in SRE, Dev Ops, Platform Engineering, or ML Engineering supporting production systems.

* Hands-on experience with:

* Kubernetes and containerization

* Cloud platforms (AWS, Azure, or GCP)

* CI/CD and observability tooling

* Proficiency in Python (and/or Java/Go).

* Working knowledge of MLOps and LLMOps practices.

* Strong understanding of security, IAM/RBAC, encryption, and AI data governance.

Clearance Requirements
* Active Top Secret clearance

* Ability to obtain and maintain required CBP BI suitability
* U.S. Citizenship required.

Preferred Qualifications
* Bachelor's degree in computer science, Engineering, or related field (preferred).

* Experience with ML platforms (MLflow, Kubeflow, Sage Maker, Azure ML, Vertex AI).

* Familiarity with data quality and orchestration tools.

* Experience with GPU orchestration and distributed processing.

* Certifications (Cloud, CKA/CKAD, Security+).

* Experience in regulated or federal…