AI/ML Engineer for Secure Production Systems (TS/SCI)

Noblis; Reston (VA); On-site; USD 110,000 - 172,000; Full time

Job summary

Noblis is seeking an experienced AI/ML Engineer with a TS/SCI clearance to support mission-critical national security initiatives. You will design, develop, and deploy advanced machine learning and AI solutions while building scalable infrastructure for secure production environments.

The role emphasizes containerized ML workloads with PyTorch, Ray, Docker, FastAPI, and Kubernetes, plus full-stack integration using Python and JavaScript in AWS-based, secure environments.

Qualifications

• Active TS/SCI clearance with a current Polygraph (required).
• Bachelor’s degree with 3 years of related experience; OR Master’s with 1 year; OR Associate’s with 6 years; OR HS diploma with 9 years.
• Demonstrated experience deploying ML models to production, including large language models.
• Experience with ML frameworks and containerization technologies (PyTorch, Docker, Kubernetes).
• Full-stack development with Python and JavaScript; working knowledge of AWS.

Responsibilities

• Design, develop, and containerize ML models using PyTorch, Ray, Docker, and FastAPI.
• Deploy, manage, and scale production ML workloads on Kubernetes.
• Integrate AI/ML into full-stack applications using Python and JavaScript.
• Develop cloud-native ML infrastructure on AWS; build MLOps pipelines.
• Provide technical leadership across AI/ML initiatives and engineering teams.

Skills

Full-stack development

DevOps

MLOps

Model deployment

Secure environments

Education

Bachelor’s degree with 3 years related experience

Master’s degree with 1 year related experience

Associate’s degree with 6 years related experience

High School diploma/GED with 9 years related experience

Tools

PyTorch

Ray

Docker

FastAPI

Kubernetes

Python

JavaScript

AWS

Job description

Noblis is seeking an experienced AI/ML Engineer with a TS/SCI clearance to support mission-critical national security initiatives. You will design, develop, and deploy advanced machine learning and AI solutions while building scalable infrastructure for secure production environments.

The role emphasizes containerized ML workloads with PyTorch, Ray, Docker, FastAPI, and Kubernetes, plus full-stack integration using Python and JavaScript in AWS-based, secure environments.