Lead the secure design, training, and deployment of a wide range of AI/ML models, ensuring they meet stringent performance, scalability, and security requirements for mission-critical applications.
Architect and manage secure automated MLOps pipelines for model monitoring, retraining, and lifecycle management to ensure continuous delivery and operational reliability.
Drive the optimization of model performance, scalability, and resource consumption in production cloud and on-premise environments.
Collaborate closely with data scientists, software engineers, and systems architects to translate model prototypes into hardened, production-grade solutions.
Champion software engineering best practices, including robust version control, comprehensive automated testing, and mature CI/CD processes.
Provide expert guidance and mentorship to other engineers on MLOps, software development, and operational best practices.
Stay current with industry trends in MLOps and operational AI to continuously evolve the team's capabilities and technical strategy.

Requirements

A Bachelor's degree in Computer Science, Engineering, or a related quantitative field with 12+ years of professional experience, or a Master's degree with 10+ years of relevant experience.
Demonstrated programming proficiency in Python and hands-on experience with major ML libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
Experience with software engineering best practices and tools, including version control, automated testing, and CI/CD pipelines.
Solid understanding of the full machine learning lifecycle, from data preparation and model training to deployment and monitoring.
A understanding of cybersecurity principles as they apply to AI systems, including threat modeling and vulnerability assessment.
Must be a U.S. Citizen and have the ability to obtain and maintain a U.S. security clearance.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

PythonTensorFlowPyTorchScikit-learnMLOpsautomated testingCI/CDmodel monitoringmodel retrainingmodel lifecycle management

leadershipmentorshipcollaborationcommunicationproblem-solvingadaptabilitystrategic thinkingguidanceteamworkbest practices advocacy