Design, train, and deploy a wide range of AI/ML models for mission-critical applications, ensuring they meet performance and scalability requirements.
Implement and manage automated MLOps pipelines for model monitoring, retraining, and lifecycle management to ensure continuous delivery and reliability.
Optimize model performance, scalability, and resource consumption in production cloud and on-premise environments.
Collaborate with data scientists, software engineers, and systems architects to translate model prototypes into hardened, production-grade solutions.
Uphold software engineering best practices, including robust version control, comprehensive testing, and CI/CD processes.
Stay current with industry trends and best practices in MLOps and operational AI to continuously improve the team's capabilities.

Requirements

A Bachelor's degree in Computer Science, Engineering, or a related quantitative field with 8+ years of professional experience, or a Master's degree with 6+ 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.
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.

PythonMLOpsAI modelsML modelsTensorFlowPyTorchScikit-learnmodel optimizationautomated testingCI/CD

collaborationcommunicationproblem-solvingadaptabilityleadership