TS/SCI AI/ML Engineer — Secure ML, Cloud & MLOps

Noblis; Reston (VA); Hybrid; USD 91,000 - 251,000; Full time

Job summary

Noblis seeks AI/ML Engineers at multiple levels with TS/SCI and Polygraph to design, develop, and deploy advanced ML/AI solutions for national security initiatives. You will build scalable infrastructure for secure, production environments and lead ML/AI efforts across teams.

Responsibilities include containerizing models, deploying on Kubernetes, and integrating AI/ML into full‑stack apps using Python and JavaScript. Remote/hybrid work status is subject to change based on requirements.

Qualifications

• Active TS/SCI with Polygraph required
• U.S. Citizenship required
• Travel up to 10% within US
• Lift up to 30 lbs, walk, bend, drive

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 applications using Python‑based backend services and JavaScript frontend technologies
• Ensure model reliability, performance, and maintainability throughout deployment lifecycle
• Architect cloud-native ML infrastructure on AWS
• Develop and maintain DevOps and MLOps pipelines
• Deploy and support AI/ML systems within secure, classified environments
• Provide technical leadership and thought leadership across AI/ML initiatives

Skills

Full-stack Dev

Python expert

ML pipelines

LLMs & RAG

Advanced ML

MLOps pipelines

Cloud ML platforms

Architecture leadership

Distributed computing

Docker

Kubernetes

Stakeholder comms

Research contributions

Education

Bachelor's degree

Master's degree

PhD

Tools

TensorFlow

PyTorch

scikit-learn

Ray

Kubeflow

SageMaker Pipelines

MLflow

Vertex AI

Job description

Noblis seeks AI/ML Engineers at multiple levels with TS/SCI and Polygraph to design, develop, and deploy advanced ML/AI solutions for national security initiatives. You will build scalable infrastructure for secure, production environments and lead ML/AI efforts across teams.

Responsibilities include containerizing models, deploying on Kubernetes, and integrating AI/ML into full‑stack apps using Python and JavaScript. Remote/hybrid work status is subject to change based on requirements.