Tech Stack

Description

You will lead a specialized team of developers and engineers dedicated to productionizing machine learning for the DoD, building and managing a centralized platform that provides access to pre-trained and custom-built AI/ML models. Your mission is to simplify model integration and accelerate the delivery of AI-powered capabilities across the enterprise, overseeing the entire lifecycle of model development, deployment, and operations.

Requirements

• 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)

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