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About This Role
Your Impact
Own your opportunity to support our nation's defense. Make an impact by connecting and securing critical operations across the globe, keeping our country safe and secure.
Job Description
As an AI Engineer for the ICAM program, you will be responsible for researching, designing, developing, and implementing Artificial Intelligence (AI) solutions that enhance Identity, Credential, and Access Management (ICAM) capabilities across the Department of War enterprise.
You will work with identity architects, cybersecurity engineers, data analysts, software developers, and mission stakeholders to identify opportunities where AI can improve identity analytics, access governance, operational efficiency, cybersecurity, and decision support.
This position is 100% remote.
MEANINGFUL WORK AND PERSONAL IMPACT:
This role will help establish and mature AI capabilities across the ICAM ecosystem by leveraging machine learning, generative AI, predictive analytics, natural language processing, and intelligent automation technologies. The AI Engineer will support the full lifecycle of AI initiatives from concept development and experimentation through production implementation and sustainment while ensuring compliance with DoW security requirements and responsible AI principles.
- Design, develop, test, and implement AI and machine learning solutions supporting enterprise ICAM objectives.
- Identify opportunities to improve identity governance, access management, onboarding processes, and operational workflows through AI\-driven capabilities.
- Develop predictive analytics, anomaly detection, behavioral analysis, and risk\-scoring models using identity and access management data.
- Support the implementation of Generative AI solutions, Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and intelligent assistants to improve operational efficiency and user support.
- Analyze large volumes of authentication, authorization, audit, entitlement, and access governance data to identify trends, risks, and actionable insights.
- Collaborate with engineers and architects to integrate AI capabilities into existing ICAM platforms and enterprise services.
- Evaluate emerging AI technologies and recommend solutions applicable to ICAM, Zero Trust, and cybersecurity use cases.
- Develop proof\-of\-concepts, prototypes, and pilot programs to validate AI capabilities before enterprise adoption.
- Support creation of AI governance processes, model validation procedures, and responsible AI controls.
- Develop technical documentation, architecture artifacts, implementation plans, and operational procedures.
- Participate in Agile development activities including sprint planning, backlog refinement, demonstrations, and retrospectives.
- Collaborate with cybersecurity teams to ensure AI capabilities align with DoW security requirements, privacy protections, and risk management frameworks.
- Support data preparation, feature engineering, model evaluation, tuning, and performance monitoring activities.
- Provide technical leadership and subject matter expertise regarding AI technologies and industry best practices.
WHAT YOU’LL NEED TO SUCCEED (Required):
Bring your expertise and drive for innovation to GDIT. The AI Engineer must have:
Education \& Experience: Bachelor’s Degree and a minimum of 8 years’ experience required. An additional 4 years of experience may be substituted in lieu of degree.
Clearance: Minimum of an active Secret security clearance required.
Certification: Minimum of an active CompTIA Security\+ certification or higher.
Technical Skills:
- 5\+ years of experience in artificial intelligence, machine learning, data science, advanced analytics, or software engineering.
- Experience designing, developing, or implementing enterprise AI solutions.
- Strong experience with Python and AI/ML development frameworks.
- Experience with machine learning libraries such as TensorFlow, PyTorch, Scikit\-learn, Hugging Face, or equivalent technologies.
- Experience developing and implementing Large Language Model (LLM) solutions, prompt engineering techniques, and Retrieval Augmented Generation (RAG) architectures.
- Experience with natural language processing (NLP), semantic search, vector databases, and knowledge retrieval technologies.
- Experience working with structured and unstructured datasets, data preparation, model training, and performance evaluation.
- Understanding of statistical analysis, predictive modeling, classification, clustering, and anomaly detection techniques.
- Experience integrating AI solutions with enterprise applications, APIs, and business processes.
- Familiarity with Microsoft Azure AI Services, Azure OpenAI, AWS Bedrock, Anthropic, OpenAI, or equivalent AI platforms.
- Proven track record supporting enterprise IT customers, preferably within DoW or Federal Government environments.
GDIT IS YOUR PLACE:
At GDIT, the mission is our purpose, and our people are at the center of everything we do.
- Growth: AI\-powered career tool that identifies career steps and learning opportunities.
- Support: An internal mobility team focused on helping you achieve your career goals.
- Rewards: Comprehensive benefits and wellness packages, 401K with company match, and competitive pay and paid time off.
- Community: Award\-winning culture of innovation and a military\-friendly workplace.
OWN YOUR OPPORTUNITY:
- Explore a career in program management at GDIT and you’ll find endless opportunities to grow alongside colleagues who share your passion for the mission and delivering results.
Work Requirements
Years of Experience
8 \+ years of related experience* may vary based on technical training, certification(s), *or* degree
Certification
CompTIA Security\+ CE \| CompTIA \- CompTIA
Travel Required
Less than 10%
Citizenship
U.S. Citizenship Required
Salary and Benefit Information
The likely salary range for this position is $153,000 \- $207,000\. 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.
View information about benefits and our total rewards program.
About Our Work
We are GDIT. A global technology and professional services company that delivers technology 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 over 50 countries worldwide, offering leading capabilities in digital modernization, AI/ML, cloud, cyber and application development. Together with our customers, we strive to create a safer, smarter world by harnessing the power of deep expertise and advanced technology.
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 $153K-$207K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. Disclosed range: $153K to $207K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 6 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span IN, US, Fort Bragg, NC, US, Tampa, FL, US. Compensation range: $103K - $207K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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