Lead Artificial Intelligence (AI) Engineer
General Dynamics Information Technology · USA VA Chantilly, United States
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:
NoneExperience:
5 + years of related experienceUS 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 secure, auditable, explainable, and compliant with applicable regulations and customer policies
- Continuous Improvement & Innovation
- - Drive continuous improvement across the portfolio by introducing modern engineering practices (MLOps, DevSecOps, CI/CD, infrastructure as code, automated testing, observability).
- Lead proof-of-concept and rapid prototyping efforts to validate new AI capabilities before scaling.
- Track industry trends and emerging AI technologies, and evaluate their applicability to the program.
- Define metrics and KPIs to measure the impact of AI initiatives on mission outcomes, user experience, and operational efficiency.
- Legacy Transition & Modernization
- - Lead technical planning and execution for transitioning legacy systems and workflows to modern architectures, including cloud-native and AI-enabled platforms.
- Perform technical and architectural assessments of legacy applications and data sources to inform migration and modernization strategies.
- Collaborate with PMO and TO leadership to sequence and manage transitions to minimize risk and disruption to operations.
- Cost, Schedule, and Performance Management
- - Support PMO and Solution Architect in estimating AI-related work, defining scope, and planning for cost-effective delivery.
- Identify and mitigate technical risks impacting schedule and performance across completion-based task orders.
- Provide regular status updates, technical roadmaps, and decision support to PMO leadership and customer stakeholders.
- Ensure AI initiatives are aligned with contractual requirements, performance objectives, and quality standards.
- Collaboration & Stakeholder Engagement
- - Work closely with TO leads, software engineers, data engineers, business analysts, and UX teams to integrate AI into end-to-end solutions.
- Engage customer stakeholders to refine requirements, demonstrate AI capabilities, and support change management and adoption.
- Mentor and guide engineering teams on AI/ML concepts, tools, and best practices, building a strong internal AI capability.
Required Qualifications
- Bachelor’s degree in Computer Science, Engineering, Mathematics, Data Science, or related field; or equivalent experience.
- 5+ years of professional experience in software engineering and/or AI/ML engineering.
- Proven experience designing, developing, and deploying AI/ML solutions in production environments.
- Hands-on experience with common AI/ML frameworks and tools (e.g., TensorFlow, PyTorch, scikit-learn, Transformers, spaCy, Hugging Face, etc.).
- Strong proficiency in at least one modern programming language (e.g., Python, Java, C#, or similar).
- Experience with cloud platforms and services (e.g., AWS, Azure, GCP) and data pipelines for AI/ML workloads.
- Demonstrated experience working in multi-project or portfolio environments (e.g., IDIQ, multi-TO, or large-scale programs).
- Familiarity with DevSecOps/CI/CD practices and tools (e.g., GitLab, GitHub Actions, Jenkins, containers, Kubernetes).
- Experience transitioning legacy applications or data systems to modern architectures.
- Strong communication skills with the ability to explain complex AI concepts to technical and non-technical stakeholders.
- Proven ability to balance innovation with delivery discipline in meeting cost, schedule, and performance goals.
Desired Skills and Experience
- Advanced degree (Master’s or PhD) in a relevant technical field.
- Experience supporting government or regulated industry customers under IDIQ or similar contract vehicles.
- Background in MLOps and observability for AI systems (model monitoring, drift detection, logging, metrics).
- Experience with data governance, responsible AI, and explainable AI (XAI) practices.
- Familiarity with enterprise architecture frameworks and portfolio management.
- Experience leading or mentoring AI/ML teams across multiple projects.
Key Competencies
- Strategic & Systems Thinking: Ability to see across task orders and engineer shared solutions and standards.
- Technical Leadership: Guides architecture and implementation of AI/ML capabilities, setting patterns and best practices.
- Inno