Senior Machine Learning Ops Engineer

Program Overview

About The Role

Peraton is seeking an experienced Senior Machine Learning Ops Engineer to support U.S. Customs and Border Protection (CBP) by ensuring the secure, reliable, and scalable operation of machine learning systems within CBP’s analytics and intelligence support programs.

This role operationalizes AI solutions by building the platforms, pipelines, monitoring, and governance controls that move models from research into mission-ready production environments. The ideal candidate combines strong reliability engineering, AI/ML lifecycle expertise, security awareness, cost optimization discipline, and cross-functional collaboration skills.

Support will be provided across multiple mission locations:

• Ashburn, VA
• Sterling, VA
• Washington, D.C.

Key Responsibilities

• Lead the architecture, design, and operation of scalable ML platforms supporting model training, batch processing, and real-time inference.
• Establish and maintain enterprise MLOps frameworks and best practices for model lifecycle management, reproducibility, and deployment.
• Design and manage CI/CD pipelines for machine learning code, data pipelines, and model artifacts.
• Deploy and operate containerized workloads using Kubernetes and cloud-native infrastructure.
• Implement automated workflows for model versioning, validation, retraining, and drift detection.
• Develop monitoring solutions for system health, model performance, latency, reliability, and operational metrics.
• Define and maintain SLOs/SLAs, supporting incident response and operational resilience for production ML systems.
• Collaborate with data scientists, data engineers, and platform teams to productionize machine learning models at scale.
• Ensure compliance with federal security, governance, and Responsible AI practices, including IAM/RBAC, encryption, and audit logging.
• Provide technical leadership and mentorship while developing reusable tools, documentation, and platform standards that improve operational efficiency.

**Position is contingent upon contract award**

Qualifications

Required Qualifications

• Minimum of 8 years with BS/BA; or 12 years with HS diploma/equivalent in lieu of a degree.
• 5+ years of experience in MLOps, DevOps, Site Reliability Engineering (SRE), or platform engineering supporting production systems.
• Experience designing and operating ML platforms or AI infrastructure in cloud environments (AWS, Azure, or GCP).
• Strong experience with Kubernetes, Docker, and containerized workloads.
• Proficiency in Python (and/or Java, Go).
• Experience implementing CI/CD pipelines, monitoring frameworks, and secure deployment practices.
• Knowledge of machine learning lifecycle management, model monitoring, and data pipeline operations.

Clearance Requirements

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

Preferred Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).
• Experience with ML platforms such as MLflow, Kubeflow, SageMaker, Azure ML, or Vertex AI.
• Familiarity with distributed training, GPU workloads, or LLMOps environments.
• Experience supporting federal, regulated, or high-security environments.
• Relevant cloud, Kubernetes, or DevOps certifications.

SCA / Union / Intern Rate or Range

Details

Target Salary Range: $104,000 - $166,000. 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

Application Duration Statement: 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.

EEO: Equal opportunity employer, including disability and protected veterans, or other characteristics protected by law.