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# AI/ML Engineer, Senior

- **Company:** Noblis Inc.
- **Location:** Chantilly, VA, United States
- **Experience:** Expert
- **Salary:** $251,325.0
- **Contract:** Permanent contract
- **Skills:** JavaScript (Programming Language), Artificial Intelligence, Amazon Web Services, DevOps, Python (Programming Language), Machine Learning, Cloud Services, Tensorflow, Pytorch, Large Language Models, Model Validation, Backend, Fastapi, Containerization, Kubernetes, Machine Learning Operations, Front End Software Development, Docker
- **Published:** August 5, 2026
- **Apply:** https://www.clearancejobs.com/jobs/9076111/aiml-engineer-senior

## About the Role

* Active Top Secret/SCI (TS/SCI) clearance with a current Polygraph
 * Bachelor's degree with 8 years of related experience; OR Master's degree with 7 years of related experience; OR associate's degree with 11 years of related experience; OR High School diploma/GED with 14 years of related experience
 * Production experience deploying ML models, including LLMs
 * Strong proficiency with ML frameworks and containerization (e.g., PyTorch, Docker, Kubernetes)
 * Full-stack development experience (e.g., Python, JavaScript)
 * Working knowledge of AWS cloud services
 * Demonstrated MLOps/DevOps implementation experience
 * U.S. Citizenship is required

#HighlyCleared
Desired Qualifications

 * AWS Certification, including AWS Certified DevOps Engineer or AWS Certified Solutions Architect Certification

## Description

Noblis is seeking an experienced AI/ML Engineer to support mission-critical national security initiatives., * Model Development & Deployment

 + Design, develop, and containerize machine learning (ML) models using modern frameworks and tools, including PyTorch, Ray, Docker, and FastAPI.
 + Deploy, manage, and sc ale 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 the deployment lifecycle.

 * Infrastructure & Operations
 + Architect and implement cloud-native ML infrastructure on AWS.
 + Develop and maintain DevOps and MLOps pipelines to streamline model development, testing, deployment, and monitoring.
 + Deploy and support AI/ML systems within secure, classified, and high side environments.

 * Technical Leadership
 + Evaluate and adopt state-of-the-art AI/ML models, frameworks, and emerging technologies.
 + Architect scalable and resilient infrastructure to support evolving AI/ML workloads and mission requirements.
 + Establish and promote best practices for production-grade machine learning (ML) systems, including security, observability, and governance.
 + Provide technical guidance and thought leadership across AI/ML initiatives and engineering teams.


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