BT-160 – AI/ML Engineer Manager
Location: Chantilly/Herndon


**MUST HAVE A TS/SCI CLEARANCE TO APPLY. Those without an active security clearance will not be considered.**


Role Description:
As the Manager for the AI/ML Models as a Service (MaaS) team, you will lead a specialized group of developers and engineers dedicated to productionizing machine learning. Your mission is to build and manage a centralized platform that provides access to pre-trained and custom-built AI/ML models, simplifying their integration and accelerating the delivery of AI-powered capabilities across the enterprise . This is a strategic, hands-on leadership role where you will define the vision for our MaaS offerings and oversee the entire lifecycle of model development, deployment, and operations.

Responsibilities:

• Lead, mentor, and manage a high-performing team of ML modeling developers and MLOps engineers.
• Define and execute the technical strategy for the MaaS platform, including the frameworks for model training, versioning, deployment, and monitoring.
• Oversee the design, development, and deployment of a diverse portfolio of machine learning models to solve complex challenges.
• Establish and enforce robust MLOps practices to ensure automated, reliable, and scalable CI/CD pipelines for machine learning models.
• Architect the service layer for the MaaS platform, ensuring models are exposed via secure, scalable, and well-documented APIs.
• Collaborate with data scientists, data engineers, and stakeholders to identify use cases and translate requirements into production-ready models.
• Implement governance, security, and ethical AI standards across the entire model lifecycle.
• Manage project timelines, resource allocation, and stakeholder communication for all MaaS initiatives.

• 8+ years of experience in data science or machine learning engineering, with at least 3 years in a technical leadership or management role.
• Deep expertise in developing and deploying ML models using common frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Proven experience building and maintaining production ML systems in a cloud environment (AWS, Azure, GCP).
• Strong understanding of MLOps principles and hands-on experience with relevant tools (e.g., MLflow, Kubeflow, AWS SageMaker, Azure ML).
• Proficiency with containerization technologies (Docker, Kubernetes) and CI/CD tools.
• Excellent programming skills in Python and familiarity with software engineering best practices.
• Active Top Secret/SCI security clearance.

• Direct experience building a Model-as-a-Service or Machine-Learning-as-a-Service platform.
• Experience with ML platforms like Databricks or AWS SageMaker AI.
• Familiarity with Infrastructure-as-Code (IaC) tools like Terraform.
• Experience working in a high-security environment.
• Demonstrated success leading teams that deliver complex, data-driven software projects.