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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)
Emerging Technologies
Software Solutions
REQ#:RQ228779
Public Trust:None
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
You will serve as a senior engineering leader responsible for building and scaling our AI‑Assisted Development capability. This role goes far beyond “using AI tools” – you will establish the strategy, operating model, practices, and technical foundations required to transform how our teams design, build, test, and deliver software solutions and products using modern AI-enabled development approaches.
You will evaluate and rationalize existing AI-enabled tools and platforms across the organization, become the internal expert on their use and integration, define best practices, upskill our engineers, and operationalize AI‑assisted development as an enterprise capability we can confidently take to market. The role blends technical leadership, architecture, go-to-market, organizational change management, and hands-on engineering expertise.
This is a highly strategic and highly technical leadership role designed for an experienced engineering leader who thrives in emerging technology spaces and enjoys scaling new capabilities from the ground up.
Education & Experience:
- BA/BS in Computer Science, Engineering, or related field (or equivalent experience)
- 5+ years of progressive software engineering experience with 3-5+ years in technical leadership roles required; 10+ years of of progressive software engineering experience with 3-5+ years in technical leadership roles preferred
- Proven experience building and leading high‑performing engineering teams, including coaching, mentorship, and capability development
- Must be able to obtain and maintain a Secret clearance
- US Citizenship Required
- Location: Remote
AI-Assisted Development Expertise:
- Strong understanding of AI-enabled development workflows, not just tool usage: prompt‑engineering patterns, code‑gen improvement loops, safety/quality validation, and architectural impacts, as well as ability to identify, track, and articulate ROI associated with AI and agentic automation
- Ability to assess organizational AI-development maturity and define roadmaps, capability models, and adoption strategies, including ability to identify and implement new operational models (i.e., team size and composition, sprint planning, code reviews, etc.) to optimize delivery
- Direct experience evaluating, selecting, and operationalizing AI-enabled development tools (GitHub Copilot, Azure AI, OpenAI models, Claude Code, etc.)
- Hands-on experience using LLM-based frameworks and retrieval-augmented generation (RAG) as part of software delivery
Capability Building:
- Ability to identify, hire, coach, and scale engineers in AI-assisted development techniques, responsible use, productivity strategies, and integration patterns
- Experience creating developer education programs, playbooks, best practices, or internal communities of practice to facilitate large-scale change management initiatives
- Experience functioning as an internal SME, providing architectural guidance on how AI changes design patterns, quality models, and delivery processes
Technical Leadership & Architecture
- Demonstrated ability to drive technical vision, architecture standards, and execution across complex, multi-team programs
- Experience leading full SDLC and product lifecycle delivery in both agile and hybrid environments
- Strong background implementing operational and engineering standards including CI/CD, DevSecOps, TDD/BDD, automation frameworks, and quality engineering practices
- Experience developing solutions within distributed, cross‑functional, or geographically dispersed teams
Software Engineering Experience:
- Full-Stack development experience including proficiency across multiple programming languages, including Java, JavaScript, Python, .NET, and others
- Hands-on experience using modern front-end and back-end frameworks (e.g., Angular, React, Spring Boot, Django, Next.js, etc.)
- Experience implementing and integrating solutions across variety of operating systems (e.g., Linux, Unix) and Platforms as a Service (e.g., RHEL, OpenShift)
- Deep experience with API/webservice design, secure coding standards, and scalable architectural patterns
Preferred Skills:
- Understanding of Federal acquisition and security compliance including FedRAMP and Authority To Operate (ATO) implications across Government Cloud and classified environments
- Experience building an enterprise AI-assisted development capability or platform offering
- Prior experience productizing internal capabilities into customer-facing solutions or services
- Familiar with Cloud-native platforms (AWS, Azure, GCP) and related AI/ML services including experience managing vendor relationships with OEM and ISVs
- Expertise with CI/CD automation, IaC, Policy as Code, and governance automation frameworks (Terraform, Ansible, etc.)
- Ability to translate technical concepts for non-technical stakeholders and influence across senior leadership levels
- Demonstrated ability to stay current with emerging AI, development, and architecture trends, and drive organizational adoption strategies
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 Flexibility: Full-flex work week to own your priorities at work and at home 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 5 + 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 $170,144 - $230,000. 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 $170K-$230K range is above the median for AI Software Engineer roles in our dataset (median: $193K across 172 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 9,653 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At General Dynamics Information Technology, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,448 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($200K) sits 9% below the category median. Disclosed range: $170K to $230K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 11 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Agent Developer, AI Software Engineer. Positions span Tampa, FL, US, Joint Base Lewis McChord, WA, US, Hurlburt Field, FL. Compensation range: $98K - $253K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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