AI/ML Engineer (Clearance Required)

Noblis•Reston, VA

1d•Hybrid

About The Position

Noblis is seeking an AI/ML Engineer with a TS/SCI with Polygraph to support mission-critical national security initiatives. In this role, you will design, develop, and deploy advanced machine learning and AI solutions while building the scalable infrastructure needed to operationalize AI capabilities in secure, production environments.

Requirements

Active Top Secret/SCI (TS/SCI) clearance with a current Polygraph
Bachelor’s degree with 3 years of related experience; OR Master's degree with 1 years of related experience; OR associate’s degree with 6 years of related experience; OR High School diploma/GED with 9 years of related experience
Demonstrated experience deploying machine learning (ML) models to production, including large language models (LLMs)
Demonstrated experience with machine learning (ML) frameworks and containerization technologies (e.g., PyTorch, Docker, and Kubernetes)
Full-stack software development experience using Python and JavaScript
Working knowledge of AWS cloud services and infrastructure
Demonstrated experience implementing MLOps and DevOps best practices
U.S. Citizenship is required

Nice To Haves

Proficiency in Python with hands-on experience using leading machine learning frameworks, including TensorFlow, PyTorch, or scikit-learn
Experience designing and implementing end-to-end machine learning (ML) pipelines, including data preprocessing, feature engineering
Familiarity with cloud-based machine learning (ML) platforms such as AWS SageMaker, Azure Machine Learning, or Google Vertex AI
Understanding of MLOps best practices, including model versioning, experiment tracking, model monitoring, and CI/CD for machine learning (ML) workflows using tools such as MLflow, Weights & Biases, or DVC
Ability to collaborate with cross-functional teams including data engineers, product managers, and domain experts to translate business problems into ML solutions
Experience working with large-scale datasets and distributed computing frameworks such as Apache Spark or Dask to support efficient data processing and model training
Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes) for scalable model serving and orchestration
Strong communication skills with the ability to explain model behavior, trade-offs, and results to both technical and non-technical stakeholders

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 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.
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 integrate state-of-the-art AI/ML models, frameworks, and emerging technologies to enhance mission capabilities and accelerate innovation.
Architect scalable, resilient, and secure infrastructure to support evolving AI/ML workloads, production deployments, and mission-critical requirements.
Establish and champion best practices for production-grade machine learning (ML) systems, including MLOps, security, observability, governance.
Provide technical guidance across AI/ML initiatives and engineering teams.