Mission-Ready AI/ML Engineer — Production & MLOps

Noblis; Reston (VA); On-site; USD 133,000 - 208,000; Full time

Job summary

Noblis is seeking an experienced AI/ML Engineer to support mission-critical national security initiatives. You will design, develop, and deploy advanced machine learning solutions while building the infrastructure required to operationalize AI capabilities in secure, production environments.

Responsibilities include model development and deployment, cloud-native ML infrastructure on AWS, MLOps pipelines, and leadership across AI/ML initiatives and engineering teams.

Qualifications

• Active TS/SCI clearance with a current Polygraph.
• 5+ years of related experience for a Bachelor’s degree, or 3+ years for a Master’s.
• Experience deploying ML models to production, including LLMs.
• Strong proficiency with ML frameworks and containerization (PyTorch, Docker, Kubernetes).
• Full-stack development with Python and JavaScript; AWS experience.
• Experience implementing CI/CD, DevOps, and MLOps best practices.

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 capabilities into full-stack apps with Python and JavaScript.
• Build cloud-native ML infrastructure on AWS and maintain MLOps pipelines.
• Provide technical leadership and promote best practices for production ML systems.

Skills

TS/SCI clearance

MLOps

CI/CD

Cloud architecture

Communication with stakeholders

Education

Bachelor’s degree in a related field

Master’s degree in related field

Associate’s degree

High School Diploma or GED

Tools

Python

JavaScript

AWS

Kubernetes

Docker

PyTorch

Ray

FastAPI

Job description

Noblis is seeking an experienced AI/ML Engineer to support mission-critical national security initiatives. You will design, develop, and deploy advanced machine learning solutions while building the infrastructure required to operationalize AI capabilities in secure, production environments.

Responsibilities include model development and deployment, cloud-native ML infrastructure on AWS, MLOps pipelines, and leadership across AI/ML initiatives and engineering teams.