Data Scientist

Noblis•Arlington, VA

7d•Onsite

About The Position

Noblis is hiring multiple AI/ML Engineers and Data Scientists for a full-time role located onsite at a Government facility in the National Capital Region (NCR). In this role, you will design, develop, and deploy advanced machine learning solutions while building the infrastructure required to operationalize AI capabilities in secure, production environments. Additionally, the Analyst shall provide support for Model Development & Deployment, Infrastructure & Operations, and Technical Leadership.

Requirements

Bachelor's degree and 5 years of experience. In lieu of a degree will also consider an Associates & 8 years of experience, or HS & 11 years of 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
Must be a US Citizen with a Top Secret Clearance & SCI eligibility

Nice To Haves

Experience with Defense systems data fusion, permissions, Authority to Operate requirements, and deployment on Government systems.

Responsibilities

Design, develop, and containerize machine learning (ML) models using modern frameworks and tools, including PyTorch, Ray, Docker, and FastAPI.
Deploy, manage, and scale production ML workloads.
Integrate AI/ML capabilities into full-stack applications.
Ensure model reliability, performance, and maintainability throughout the deployment lifecycle.
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.
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.