Lead Artificial Intelligence (AI) Engineer

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 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.
• •Innovation &