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Lead Artificial Intelligence; AI) Engineer

Job in Chantilly, Fairfax County, Virginia, 22021, USA
Listing for: General Dynamics Information Technology, Inc.
Full Time position
Listed on 2026-10-04
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 169604 - 229464 USD Yearly USD 169604.00 229464.00 YEAR
Job Description & How to Apply Below

Type of

Requisition :
Pipeline Clearance Level Must Currently Possess:
Top Secret SCI + Polygraph Clearance Level Must Be Able to Obtain:
Top Secret SCI + Polygraph Public Trust/Other

Required:

None Job Family:
Data Science and Data Engineering

Job Qualifications:

Skills:

AI Concepts, AI Systems, Artificial Intelligence (AI), Data Science, Machine Learning (ML)

Certifications:

None

Experience:

5 + years of related experience US Citizenship

Required:

Yes

Job Description:

Why GDIT Are you ready to be a part of an elite team at GDIT, working on a large-scale, pioneering National Intelligence program? This is an incredible opportunity to immerse yourself into an environment that fuses innovation, speed, and security to safeguard our Nation. At GDIT, you'll thrive in a dynamic and collaborative setting, where your technical skills will be both challenged and expanded.

This program offers the chance to engage with cutting-edge technologies and contemporary development practices in support of a vital mission. You'll play a critical role in addressing some of the most intricate security and operational challenges facing Intelligence and Homeland Security today. Come join us and contribute to a mission that truly matters, while advancing your career alongside some of the brightest minds in the industry.

What

You’ll Achieve
  • The Lead AI Engineer will serve as the innovation and technical AI lead across a six–Task Order (TO) software-development IDIQ portfolio. Operating within the Program Management Office (PMO) and reporting directly to the Solution Architect, this role is responsible for:
  • Rationalizing and optimizing the solution portfolio.
  • Guiding AI/ML and automation insertion across multiple TOs.
  • Driving continuous improvement in delivery processes and technical solutions.
  • Leading selection of AI models based on use case and performance requirements, including model optimization and tuning.
  • Exploring use of open-weight/open-source models to reduce token consumption across the program and TOs.
  • Collaborating with customer on adoption of new and emerging AI capabilities.
  • Ensuring cost, schedule, and performance objectives are met across completion-based task orders.

The ideal candidate combines deep AI/ML engineering expertise with strong systems‑thinking, software delivery experience, and the ability to influence stakeholders across a complex program environment.

Key Responsibilities
  • Portfolio‑Level AI Leadership:
    Develop and maintain an AI/ML strategy for the six–Task Order IDIQ portfolio, aligned with enterprise architecture and program objectives. Assess current systems and capabilities to identify opportunities for AI‑driven enhancements, cost savings, and performance improvements. Rationalize overlapping solutions and tools across task orders, driving reuse, common services, and standardized approaches to AI/ML.
  • AI Insertion & Technical Execution:
    Architect and guide the design, development, integration, and deployment of AI/ML solutions (e.g., predictive analytics, NLP, recommendation engines, intelligent automation) into existing and new applications. Leverage GDIT enterprise accelerators—including ALAMO, Coral, and SDAF—to rapidly design, prototype, and operationalize AI capabilities across the portfolio. Coordinate with corporate reach‑back and centralized GDIT accelerator teams to ensure effective adoption, configuration, and continuous enhancement of ALAMO, Coral, and SDAF within program solutions.

    Partner with individual TO technical leads to define use cases, data requirements, model selection, training pipelines, and MLOps practices. Establish and enforce best practices for AI model lifecycle: experimentation, evaluation, deployment, monitoring, retraining, and retirement. Ensure AI solutions are…
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