Senior AI ML Engineer

About the role

MANTECH seeks a motivated, career and customer-oriented Senior AI ML Engineer. This is a hybrid position with two to three days onsite in Ashburn, VA. You will collaborate within a cross-functional team to develop new Artificial Intelligence/Machine Learning (AI/ML) based solutions into operational pipelines to deliver mission impact for U.S. Customs and Border Protection (CBP). Each day CBP oversees the massive flow of people, capital, and products that enter and depart the United States via air, land, sea, and cyberspace. The volume and complexity of both physical and virtual border crossings require the application of solutions to aid officers in detecting threats while promoting efficient trade and travel.

Responsibilities

• Lead the integration and deployment of trained AI/ML models into production environments (e.g., cloud, edge devices) using MLOps best practices.
• Develop and optimize model training & inference pipelines for real-time execution, and efficiently handle large-scale data processing.
• Work with data science teams to structure automated ML model health monitoring and refresh capabilities.
• Implement continuous integration, delivery and training (CI/CD/CT) workflows with commercial and open-source modeling platforms/services.
• Coordinate with Data Science and Engineering teams to build scalable feature stores for optimal model training & execution workflows.
• Research, evaluate and recommend new tools, applications, software packages for MLOps engineering that can be adopted and approved for use in the CBP environment.
• Collaborate with cross-functional teams (e.g., Software Engineering, Data Science) to integrate and test multiple candidate AI/ML models and applications for operational assessment.

Requirements

• HS Diploma/GED and 15‑20 years of experience, AS/AA and 13‑18 years, BS/BA and 7+ years or MS/MA/MBA and 5+ years or PhD/Doctorate and 3+ years.
• Hands‑on experience with LLMs such as Gemini, Llama, Mistral, or other open‑source and commercial models.
• Experience with LLM application frameworks such as LangChain, LlamaIndex, or equivalent custom frameworks.
• Ability to optimize LLM systems for latency, throughput, scalability, reliability, GPU utilization, and inference cost.
• Experience deploying machine learning or LLM services in AWS, Azure, or Google Cloud.