AI / ML Engineer Manager

About the Role 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 for the DoD. 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 mission 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 mission 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. Required Qualifications 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 . Experience with programming skills in Python and familiarity with software engineering best practices. US Citizenship (No Dual Citizenship) Preferred Qualifications 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 DoD or Intelligence Community environment. Demonstrated success leading teams that deliver complex, data-driven software projects. Security Clearance Active TS or TS/SCI Clearance #J-18808-Ljbffr