AI/ML Engineer (Clearance Required)

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.
• 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.

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.

Qualifications

• U.S. Citizenship is required.

Desired Qualifications

• 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.