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About This Role
Clearance Level None Category Technical Solutions Location Falls Church, Virginia (Hybrid Workplace)
Key Skills For Success Architecture Governance
Information Technology Consulting
IT Innovation
Solutions Management
REQ#:RQ228871
Public Trust:None
Requisition Type:Regular
Your Impact
Own your opportunity to be at the center of GDIT’s business operations. Make an impact by collaborating across functions to make mission success achievable.
Job Description
GDIT is seeking a Senior AI Partner Sales Engineer to serve as the technical co‑pilot to the AI Alliance Partner Manager. You will translate partner technologies into actionable solutions for GDIT by driving PoCs and Builds, architecting integrations, building innovative AI solutions, and enabling internal teams.
This role centers on internal technical leadership—building awareness, organizing technical engagements, and helping divisions adopt partner AI, autonomy, and data platforms. Coding is optional; what matters is understanding the technology, caring about its mission value, and showcasing it effectively.
Partner Recruitment & Technical Vetting
- Support identification and recruitment of high‑value AI/data/autonomy partners.
- Evaluate partner technical capabilities, architectures, and integration approaches.
- Deliver early-stage demos that highlight partner differentiation and mission fit.
Joint Solution Architecture & POCs
- Co‑design and document joint solution architectures, reusable patterns, and PoC frameworks.
- Organize and coordinate internal PoCs and Builds with partner and GDIT teams.
- Integrate partner technologies into GDIT offerings and Digital Accelerators.
- Develop crisp demo assets, sales kits, and technical placemats.
Internal Enablement & Awareness
- Plan and deliver workshops, hackathons, Technology Days, and demos across divisions.
- Build certification paths and training roadmaps with GDIT enablement teams.
- Drive understanding and adoption of partner capabilities across internal stakeholders.
Technical Escalation & Integration Support
- Serve as the single technical point-of-contact for partner-related issues and questions.
- Troubleshoot integration challenges and coordinate resolution with partner engineering teams.
- Provide real-time demo and solution support during partner/customer engagements.
- Maintain partner technical scorecards across products and platforms.
Strategic Partner Leadership
- Collaborate with the Partner Manager on solution strategies involving co‑selling, co‑marketing, and co‑delivery.
- Support strategic accounts, captures, and proposals involving partner technologies.
- Build internal demand pipelines by aligning partner capabilities with mission needs.
Required Qualifications
- Bachelor’s with 12+ years in technology consulting, pre-sales engineering, partner engineering, or solutions architecture or Master's with 8+ years
- 3–5+ years in technical partner roles, pre-sales engineering, or solution architecture.
- Strong understanding of cloud architectures, API integrations, modern development workflows, and proprietary platforms (e.g., Hivemind, Echelon, Foundry, Lattice).
- Experience with major AI/data platforms (Palantir, Databricks, Snowflake, Anthropic, xAI, OpenAI).
- Familiarity with AWS services (Kiro, SageMaker, Bedrock, Q, EMR) and AWS partner programs.
- Demonstrated success supporting AI/data solutions for public sector missions.
- Exceptional communication and demo skills, from technical deep-dives to executive briefings.
- Location: Washington DC Metro (hybrid)
- The selected candidate will need to obtain & maintain a Secret security clearance.
- Due to the need for a security clearance, US Citizenship is required.
Preferred Attributes
- Comfortable with impromptu demos, rapid PoCs, and unscripted technical discussions.
- Balances deep technical problem-solving with business growth awareness.
- Builds trusted relationships with both internal teams and partner technical leaders.
What GDIT Can Offer You
- A highly dynamic, collaborative environment where leaders can drive meaningful impact.
- Full-flex work week to support balance and autonomy.
- Competitive 401(k) with company match.
- Comprehensive health and wellness programs.
- Internal mobility support and dedicated career development opportunities.
- Professional growth, paid education, and certifications.
- Access to cutting-edge partner technologies and platforms.
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 $187,179 - $253,000. 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 $187K-$253K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $187K to $253K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 9 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, 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% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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