Machine Learning Engineer

Required Qualifications

• Collaborate with cross-functional teams—including data scientists, engineers, and architects—to build machine learning models, training tools, and simulation environments that support business functions.
• Implement the full MLOps lifecycle to deploy, operationalize, scale, and manage automated machine learning models and analytical solutions.
• Develop and test ML applications according to requirements, running targeted experiments to optimize overall system performance.
• Train and embed machine learning models into applications using programming languages (Python, Java, R) and core libraries (TensorFlow, Keras, Scikit-learn).
• Explore and visualize data to uncover key insights and identify specific factors that impact model accuracy and performance.
• Manage and deploy cloud-based ML services across major cloud computing environments, including AWS, Azure, or Google Cloud Platform (GCP).
• Follow Agile methodologies to deliver production-ready, highly testable code in small, efficient, and continuous iterations.
• Master’s degree in Applied Mathematics, Statistics, Computer Science, or related fields.
• Verifiable work experience defining and implementing data pipelines and machine learning algorithms.
• Proficiency in Agile Development and Git Operations.
• Experience creating, maintaining, and communicating complex technical documentation for machine learning systems.
• Experience with the Sponsor’s primary cyber risk and compliance automation tools.
• An active Secret clearance is required.
• Must be able to remain in a stationary position 50%.
• Needs to occasionally move about inside the office to access file cabinets, office machinery, etc.
• Frequently communicates with co-workers, management, and customers, which may involve delivering presentations.
• Must be able to exchange accurate information in these situations.