ML/OPs Engineer

Ensure the safety and security of our nation as a ML/OPs Engineer at GDIT. You'll apply the latest technology and provide operational support to deliver actionable intelligence for the Intelligence Community. Here, your work will have meaning and impact as you deliver your best every day.

The ML/OPs Engineer will deploy, fine-tune, and monitor production machine learning models in a production environment. Additionally, they will provide support in the areas of data extraction, transformation and load (ETL), data mapping, analytics, operations, databases, and maintenance of data and associated systems. As a member of the team, you will work in a multi-tasking, quick-paced, dynamic, process-improvement environment that requires experience with the principles of data science, data modeling, data mapping, data testing, data quality, and documentation preparation. This is a mission-focused role requiring experience with deploying models in a production environment against real-time collection.

Responsibilities:

• Create and maintain custody of production machine learning models across a variety of tasks, including but not limited to audio extraction, object recognition, Natural Language Processing (NLP), and other generic classification tasks
• Optimize existing machine learning services to better utilize current GPU capabilities and assist with road mapping future GPU requirements
• Deploy machine learning models against streaming data, designed to provide near-real-time analytics to augment decision-making
• Improve data architecture decisions with data engineers to better stage data for continuous training models in production
• Provide support in the areas of data extraction, transformation and load (ETL), data mapping, analytics, operations, databases, and maintenance of data and associated systems

Requirements:

• An active TS/SCI with Polygraph security clearance
• Fifteen (15+) years of related experience
• Develop and manage infrastructure for model training, deployment, and serving across on-prem environments
• Manage containerization and orchestration of ML services (e.g., Docker, Kubernetes)
• Build and maintain CI/CD pipelines for machine learning models and data workflows
• Collaborate with data scientists, data engineers, and software developers to operationalize ML solutions
• Troubleshoot production issues and optimize system reliability, scalability, and cost
• Document processes, standards, and best practices for ML operations
• Automate model packaging, versioning, testing, and release processes
• Implement monitoring for model performance, data quality, drift, latency, and system health
• Maintain reproducible environments and experiment tracking for model development
• Support data and model governance, including security, access controls, and auditability

Benefits:

• 401K with company match
• Comprehensive health and wellness packages
• Internal mobility team dedicated to helping you own your career
• Professional growth opportunities including paid education and certifications
• Cutting-edge technology you can learn from
• Rest and recharge with paid vacation and holidays