AI​/ML Engineer Manager

BT-160 – AI/ML Engineer Manager

Location:

Chantilly/Herndon

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 product ionizing 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.

• 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., Tensor Flow, 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 Sage Maker, 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 Sage Maker 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.