Interested in this AI/ML Engineer role at General Dynamics Information Technology?
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About This Role
Clearance Level Top Secret SCI + Polygraph Category Data Science and Data Engineering Location Chantilly, Virginia (Onsite Workplace)
Key Skills For Success AI Concepts
AI Systems
Artificial Intelligence (AI)
Data Science
Machine Learning (ML)
REQ#:RQ229558
Public Trust:None
Requisition Type:Pipeline
Your Impact
Own your opportunity to serve as a critical component of our nation’s safety and security. Make an impact by using your expertise to protect our country from threats.
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 & Continuous Improvement: Drives experimentation and adoption of modern AI and engineering practices.
- Stakeholder Management: Engages customer and internal stakeholders effectively, managing expectations and risks.
- Delivery Discipline: Maintains focus on cost, schedule, and technical performance across completion-based task orders.
Work Requirements
Years of Experience 5 + years of related experience
- may vary based on technical training, certification(s), or degree
Certification Travel Required None
Citizenship U.S. Citizenship Required
Salary and Benefit Information
The likely salary range for this position is $169,604 - $229,464. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range.
Our Identity Verification Process
As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during virtual interviews. We reserve the right to take your picture to verify your identity and prevent fraud. By proceeding, you authorize the collection, processing, and use of your biometric data for identity verification and security purposes.
About Our Work
We are GDIT. A global technology and professional services company that delivers technology solutions and mission services to every major agency across the U.S. government, defense and intelligence community. Our 26,000 experts extract the power of technology to create immediate value and deliver solutions at the edge of innovation. We operate across 50+ countries worldwide, offering leading mission-ready capabilities in AI, cloud, cyber and software development.
Join our Talent Community to stay up to date on our career opportunities and events at gdit.com/tc.
Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans
Salary Context
This $169K-$229K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At General Dynamics Information Technology, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($199K) sits 6% below the category median. Disclosed range: $169K to $229K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 11 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Agent Developer, AI Software Engineer. Positions span Tampa, FL, US, Joint Base Lewis McChord, WA, US, Hurlburt Field, FL. Compensation range: $98K - $253K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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