AI/ML DevOps Engineer

bcore • McLean, VA

1d

About The Position

AI/ML DevOps Engineer McLean, VA TS/SCI with Poly At Bcore, our strength comes from how we deliver impact to the mission. Whether it’s architecting critical IT solutions, producing actionable intelligence, or developing cutting edge technology, we succeed because of the expertise, collaboration, and agility of our teams. Our Mission Services division combines enterprise IT, cloud solutions, DevSecOps, systems engineering, software development, and operational support. Bcore accelerates decisive advantage for warfighters and intelligence professionals by fusing human insight, rapid-fire engineering, precision-measured outcomes, and relentless grit into mission-ready solutions. Do you want to join a team that is building tailored technical solutions to modernize our government’s mission and our client’s business? Do you have a desire to change how people work? Are you interested in helping to protect our nation’s cyber interests? Join our growing team supporting customer missions as a AI/ML DevOps Engineer in McLean, Virginia .

Requirements

• Understanding of ML model lifecycles, common NLP tasks, and metrics.
• Experience with CI/CD pipelines, containerization, orchestration, and cloud platforms.
• Strong proficiency with Python and IaC tools (Terraform, CloudFormation, etc.).
• Promote DevOps and MLOps best practices, including CI/CD, IaC, and automated testing.
• Evaluate emerging tools and methodologies for AI model monitoring and maintenance.

Nice To Haves

• Implementing model drift detection or performance monitoring mechanisms.
• Working with distributed systems and scalable microservices architectures.
• Background with LLM or NLP-specific evaluation frameworks is a plus.

Responsibilities

• Develop and maintain automated pipelines for model validation, continuous evaluation, and performance monitoring.
• Implement systems to detect and quantify model drift, degradation, and changes in data distributions.
• Build automated workflows for scheduled or event-driven model retraining and deployment.
• Design scalable, reliable infrastructure to support the end-to-end AI/ML lifecycle.
• Create frameworks for model documentation, versioning, and artifact management.
• Integrate monitoring tools to track model KPIs, system health, and demand signals.
• Partner with AI/ML and NLP teams to operationalize state-of-the-art techniques for accuracy assessment and benchmarking.
• Ensure reliable, reproducible model deployment processes across environments.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 51-100 employees