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About This Role
Clearance Level
None
Category
Data Science and Data Engineering
Location
Remote, Working from the USA
Key Skills For Success
Amazon Bedrock
Amazon Web Services (AWS)
Artificial Intelligence (AI)
Machine Learning (ML)
Retrieval\-Augmented Generation
##### REQ\#:RQ219814
##### Public Trust:BI Full 6C (T4\)
##### Requisition Type:Regular
##### Your Impact
Own your opportunity to work alongside federal civilian agencies. Make an impact by providing services that help the government ensure the well being and support of U.S. citizens.
Job Description
-------------------
Own your opportunity to turn data into measurable outcomes for our customers’ most complex challenges. As an Artificial Intelligence (AI)/Machine Learning (ML) Technical Lead at GDIT, you’ll power in joining our team to support the Centers for Medicare and Medicaid Services (CMS). Work Visa sponsorship will not be provided.
At GDIT, people are our differentiator. As an AI/ML Technical Lead supporting CMS, you will architect, develop, integrate, and operationalize AI/ML Technical Lead capabilities that enhance decision support and operational readiness across environments. You will be responsible for designing, developing and deploying AI models, algorithms and systems to enhance productivity, decision\-making and automation within the organization.
Key Responsibilities
- Serve as the technical lead for AI/ML strategy, architecture, and implementation across the program.
- Design and deploy scalable AI/ML models in support of the CMS mission.
- Lead development of predictive analytics, automation frameworks, and intelligent decision‑support systems.
- Integrate AI solutions into secure enterprise and tactical environments.
- Ensure compliance with CMS cybersecurity and data governance standards.
- Provide oversight of data engineering pipelines and model lifecycle management (MLOps).
- Develop and maintain model validation, explainability, and bias mitigation practices.
- Advise program leadership and stakeholders on emerging AI technologies and mission applications.
- Support transition of experimental or prototype AI capabilities into operational production systems.
- Integrate security, ethical, and data governance requirements directly into AI development workflows, ensuring alignment with CMS Risk Management Framework expectations.
- Drive AI security risk management by assessing threats and producing required ATO, configuration management, and incident response documentation.
- Coordinate with ISSOs, business owners, and other stakeholders to implement secure AI solutions and participate in audits, interviews, and governance reviews.
- Lead engagement in security governance processes to maintain continuous compliance with CMS cybersecurity and operational standards.
- Mentor junior engineers and provide technical leadership across cross‑functional teams.
Required Qualifications
- Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, or related technical field (Master’s or PhD preferred).
- 10\+ years of progressive experience in AI/ML, data science, software engineering, or related advanced analytics disciplines, including hands‑on experience with modern AI technologies.
- This is a hands on development position must have experience developing AI/ML solutions.
- 5\+ years of hands\-on experience designing, developing, and deploying AI/ML solutions in production environments, including experience with modern AI technologies and MLOps practices.
- Proficiency in programming languages such as Python, R, and/or Java.
- Deep understanding of machine learning frameworks such as TensorFlow or PyTorch, preferably implemented within AWS SageMaker.
- Experience integrating AI/ML capabilities into secure enterprise environments and adhering to cybersecurity, data governance, and compliance requirements.
- Experience developing AI products or functional prototypes using Retrieval‑Augmented Generation (RAG) and agentic AI technologies.
- Experience working with cloud computing platforms (AWS preferred; Azure or Google Cloud acceptable) and using AI services such as AWS Bedrock, Azure AI Foundry, or Google Vertex AI.
- Experience implementing AI solutions in secure, restricted or regulated environments.
- Candidate must be able to obtain Public Trust clearance.
- Candidate must have lived in the United States at least three (3\) out of the last five (5\) years.
Preferred Qualifications
- Experience supporting enterprise\-scale Salesforce Solutions; Agentforce implementation experience is highly preferrable.
- Familiarity with AI governance practices and modernization initiatives, including efforts to align AI capabilities with enterprise transformation goals.
- Experience working with or supporting U.S. Federal Agencies, preferably the Centers for Medicare and Medicaid Services (CMS).
- Experience deploying AI/ML solutions in cloud environments such as AWS GovCloud or Azure Government.
- Knowledge of Zero Trust architectures and the integration of cybersecurity principles into AI/ML systems.
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 data science and engineering at GDIT and you’ll find endless opportunities to grow alongside colleagues who share your determination for solving complex data challenges.
### Work Requirements
Years of Experience
10 \+ years of related experience
- may vary based on technical training, certification(s), *or* degree
Certification
Travel Required
Less than 10%
### 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.
### 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 $153K-$207K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($180K) sits 18% below the category median. Disclosed range: $153K to $207K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 14 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist. Positions span Bethesda, MD, US, Springfield, VA, US, Fort Bragg, NC, US. Compensation range: $164K - $304K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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