Interested in this AI Architect role at General Dynamics Information Technology?
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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 Artificial Intelligence (AI)
Cloud Development
Machine Learning (ML)
REQ#:RQ230320
Public Trust:BI Full 6C (T4)
Requisition Type:Regular
Your Impact
Own your opportunity to support the missions that matter. From working with technologies like AI, cyber and cloud to careers in intelligence and health, we offer endless opportunities to apply your expertise to create a safer, smarter world while building new skills to propel your career forward.
Job Description
Seize your opportunity to make a personal impact supporting the Case Management Modernization (CMM) Program. The CMM Program is an initiative to support the Administrative Office of the US Courts (AO) in developing a modern, cloud-based solution to support all 204+ federal courts across the United States.
GDIT is your place to make meaningful contributions to challenging projects and grow a rewarding career. The AI/ML Architect SME will be responsible for defining, designing, deploying, and maintaining the modular AI/ML architecture necessary for the CMM Program. They will architect and implement end-to-end AI/ML pipelines that include data ingestion, feature engineering, model training, deployment, monitoring, and retraining, ensuring the continuous effectiveness and improvement of these solutions. This role provides technical leadership, strategic direction, and tool recommendations for AI/ML architecture to improve the architecture, automation, and delivery of AI/ML products.
We are seeking a candidate with recent, demonstrable, hands-on experience in the AI/ML field to play a key role in the development of this exciting modernization effort that will have far-reaching impacts across the full breadth of the US federal courts system. If you’re interested in this exciting opportunity and would like to grow your AI/ML career with a company that always puts its people at the forefront of everything we do, then we’d love to hear from you!
RESPONSIBILITIES:
- Define, document, and maintain a scalable, modular AI/ML architecture that aligns with enterprise cloud strategy and product requirements for the CMM Program.
- Architect and implement end-to-end AI/ML pipelines including data ingestion, feature engineering, model training, deployment, monitoring, and retraining.
- Integrate MLOps best practices for continuous integration, delivery, and lifecycle management of machine learning models
- Ensure multi-tenant and multi-region AI/ML workloads are elastic, highly available, and cost-efficient
- Leverage Infrastructure-as-Code (IaC) tools to provision and manage cloud-based AI/ML infrastructure in accordance with federal compliance and security standards
- Design and support reusable, secure, and performant AI/ML services and APIs to be consumed by enterprise applications
- Conduct model validation and optimization in cloud environments, ensuring responsible AI practices, fairness, and transparency
- Maintain AI/ML architecture documentation and update artifacts per sprint cycles or as changes occur
- Support the inclusion of AI/ML metrics, resource utilization, and performance KPIs in technical dashboards and reports
- Guide teams on the selection and integration of third-party ML tools, frameworks, or SaaS offerings in a secure and compliant manner
REQUIRED EXPERIENCE & QUALIFICATIONS:
- Education: BA/BS or equivalent required; Master of Arts or Science in related field preferred
- Experience: 15+ years of related experience required, with at least 5+ years of recent, demonstrable, hands-on experience architecting and deploying AI/ML workflows in cloud environments utilizing Amazon SageMaker AI, Azure Machine Learning, and/or Google Cloud Vertex AI
- Experience may be considered in lieu of degree as follows: HS (19+ years), AA/AS (17+ years), BA/BS (15+ years), MA/MS (13+ years), Doctorate Degree/Ph.D. (12+ years)
- Proven success in architecting and deploying AI/ML workflows in cloud environments (Amazon SageMaker AI, Azure Machine Learning, and/or Google Cloud Vertex AI)
- Hands-on experience with ML/DL frameworks such as TensorFlow, PyTorch, scikit-learn, XGBoost, or Keras
- Strong programming skills in Python, with experience in containerization (Docker) and orchestration (Kubernetes) for ML workloads
- Experience with MLOps tools (MLflow, Kubeflow, TFX, Airflow) and CI/CD integration for model deployment
- Familiarity with federal data governance, security, and privacy standards, including JISF, NIST 800-53, and FedRAMP
- Proficient in using IaC tools such as Terraform, CDK, or CloudFormation to automate cloud ML infrastructure
- Experience with multi-tenant, distributed systems, and cloud-native architecture patterns
Preferred Certification(s):
One or more of the following certifications is preferred but not required.
- AWS Certified Machine Learning Engineer – Associate
- AWS Certified Machine Learning – Specialty
- Microsoft Certified: Azure AI Apps and Agents Developer Associate
- Microsoft Certified: Machine Learning Operations Engineer Associate
- Google Cloud Professional Machine Learning Engineer
Security Clearance Level: Must be able to pass a background check to obtain a position of Public Trust.
Must be a US Person (Green Card Holder, US Permanent Resident Alien, Refugee, Asylee, or US Citizen).
Location: Remote.
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.
#GDITLA
Work Requirements
Years of Experience 15 + 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 $170,144 - $212,750. 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 $170K-$212K range is above the median for AI Architect roles in our dataset (median: $175K across 37 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,079 AI roles we're tracking, AI Architect positions make up 1% of the market. At General Dynamics Information Technology, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $224,070 based on 236 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($191K) sits 15% below the category median. Disclosed range: $170K to $212K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 11 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Architect, Data Scientist. Positions span Falls Church, VA, US, Sterling, VA, Remote, US. Compensation range: $98K - $253K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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