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About This Role
Clearance Level
Top Secret/SCI
Category
Technical Solutions
Location
Herndon, Virginia
*(Hybrid Workplace)*
Key Skills For Success
Cloud Platform
Data Science
Data Systems
PyTorch
Shipley Proposal Writing
##### REQ\#:RQ210083
##### Public Trust:None
##### Requisition Type:Regular
##### 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
-------------------
Join GDIT’s Intelligence and Homeland Security (IHS) CTO organization and lead AI/ML technical solutioning for data\-rich ecosystems in support of our customers’ most critical missions. This role as highly technical Director, Lead Solution Architect AI/ML, combines deep expertise in artificial intelligence, machine learning, data science, and modern software delivery with the strategic acumen of a solutions architect. The candidate will will analyze RFI and RFP requirements and develop responsive solutions including staffing and other costs, review solutions developed by others, present solutions to internal teams and clients, develop and explain use\-cases and will work closely with capture and proposal teams. Candidate will develop logical and physical architectures and designs. Candidate will author and lead proposal materials including writing proposal sections and diagrams and review the work of others in this area.
How a Director, Lead Solution Architect AI/ML will make an impact:
Proposal and Solution Leadership
- Lead technical solution strategy throughout the Capture and Proposal lifecycle, from early Qualification through Proposal submission.
- Contribute to GDIT win strategies and lead the development of technical win themes and solution readiness plans.
- Engage current and prospective customers to understand critical needs, socialize and vet solutions, and advise on strategies to shape acquisitions that achieve desired results.
- Identify and select the best technical solutions and approaches to meet anticipated or actual acquisition requirements.
- Lead solution design and the development of key artifacts including, but not limited to architectural models, process diagrams, concepts of operation, staffing plans etc.
- Develop unsolicited technical recommendations, collateral and RFI/SSN responses to engage prospective customers and position GDIT to win.
- Collaborate with GDIT technical service areas and lines of business to identify and bring the best technical and business strategies and solutions to differentiate GDIT.
- Contribute to teaming strategies including identifying and vetting potential partners.
- Develop cost strategies, LOEs/BOEs/BOMs, and associated rationale.
- Ensure readiness of solution and proposal support team to prepare a compelling and compliant response to the government’s solicitation.
- Lead development of the Technical volumes and orals presentations for proposals.
- Coach and advise more junior solution architecture and proposal support resources.
- Ensure integration of technical solution with all sections and volumes of the proposal.
- Review proposals and provide critical feedback needed to strengthen our solution and proposals
- Author Technical volume introductions, key sections and orals slides including developing compelling graphics.
WHAT YOU’LL NEED TO SUCCEED:
The candidate must possess the following skills:
- 10\+ years of professional experience in data science and AI/ML engineering and/or Data Science.
- 15\+ years of professional experence in federal proposal solutioning with successful captures in the Intelligence Community and/or Department of Homeland Security.
- Bachelor of Science in Computer Science, Information Technology, similar discipline or equivalent experience.
- Experience with contributing to Federal solicitation responses.
- Experience working with large data sets including data integration, data migration, analysis and visualization.
- Experience with cloud\-native data analytic solution architectures.
- Experence with architecting end\-to\-end AI/ML systems, from data ingestion pipelines and feature stores to model training, evaluation, and deployment in production environments.
- Programming: Expert\-level Python proficiency; strong familiarity with C\+\+, Go, or Java for integration and performance\-critical workloads.
- Frameworks: TensorFlow, PyTorch, JAX, ONNX, Hugging Face Transformers, scikit\-learn.
- Data Science: NumPy, pandas, SciPy, scikit\-learn, LangChain, R, SQL
- MLOps Tools: MLflow, Airflow, Kubeflow, DVC, BentoML, Weights \& Biases.
- Data Systems: Spark, Databricks, Kafka, Delta Lake, Snowflake, or BigQuery.
- Cloud Platforms: AWS (SageMaker, Bedrock), Azure (Machine Learning, Synapse), or GCP (Vertex AI, Dataflow).
- Infrastructure: Docker, Kubernetes, Terraform, Helm, GPU/TPU orchestration.
- Security/Compliance: IAM, key management, audit logging, AI model explainability, and responsible AI design to include the design of Agentic guardrails
- Communcation: Ability to write exceptionally and to create compliant and compelling narrative that best presents GDIT’s solutions and approaches within the government’s requirements and evaluation criteria coupled with the ability to conceptualize and communicate or develop rich graphic visuals that help to strengthen the GDIT story and clearly and effectively communicate concepts and approaches
- US Citizenship required
- Clearance: Candidates must have an active Top Secret/SCI Clearance, with a Poly clearance strongly preferred.
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
- 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
### 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%
Citizenship
U.S. Citizenship Required
### Salary and Benefit Information
The likely salary range for this position is $225,250 \- $304,750\. 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 $225K-$304K range is above the 75th percentile 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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($265K) sits 21% above the category median. Disclosed range: $225K to $304K.
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.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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