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About This Role
An Iron EagleX Opportunity
Iron EagleX, a GDIT company, contributes to the U.S. government’s mission of protecting our nation and enables our customers to make quicker decisions and act faster than our adversaries.
Clearance Level
Top Secret/SCI
Category
Software Engineering
Location
Fort Bragg, North Carolina
*(Hybrid Workplace)*
Key Skills For Success
DevSecOps
Large Language Model (LLM) Fine\-Tuning
Machine Learning Operations
Python (Programming Language)
Relational Database
##### REQ\#:RQ223333
##### Public Trust:None
##### Requisition Type:Regular
##### Your Impact
Own your opportunity to support our nation's defense. Make an impact by connecting and securing critical operations across the globe, keeping our country safe and secure.
Job Description
-------------------
YOUR IMPACT
Own your opportunity to work with the largest government agency in the nation. Make an impact by advancing the Department of War’s mission to keep our country safe and secure.
OUR COMPANY
Iron EagleX (IEX), a wholly owned subsidiary of General Dynamics Information Technology (GDIT), delivers agile IT and Intelligence solutions. Combining small\-team flexibility with global scale, IEX leverages emerging technologies to provide innovative, user\-focused solutions that empower organizations and end users to operate smarter, faster, and more securely in dynamic environments.
JOB DUTIES
- Lead the technical integration of modern AI and large language model capabilities into existing applications and workflows, including prompting, tool calling, retrieval\-augmented generation (RAG), and related patterns.
- Develop clean, maintainable, and efficient Python code while adhering to best practices and coding standards.
- Lead the technical design and implementation of AI\-enabled web applications and analytics using Streamlit, React, or similar technologies.
- Design, develop, and integrate APIs, including FastAPI\-based services, to enable seamless communication between systems.
- Integrate and maintain user interfaces that make complex AI, data, and analytics capabilities accessible to end users.
- Establish and maintain CI/CD pipelines and container\-based deployments using tools such as Docker to support repeatable builds and reliable releases.
- Work with relational databases such as PostgreSQL and MySQL to ensure data integrity, performance, and efficiency.
- Implement and maintain version control using Git to streamline collaboration and code management.
- Collaborate with cross\-functional teams to define, design, develop, test, and deliver new features.
- Ensure all developed solutions meet high standards for security, quality, reliability, and maintainability.
- Contribute to all stages of the software development life cycle, from concept and design through testing, deployment, and sustainment.
REQUIRED SKILLS
- Proficiency in writing clean, maintainable, and efficient Python code.
- Experience building and integrating AI\-enabled web applications, analytic dashboards, or user workflows using Streamlit or similar technologies.
- Strong knowledge of web technologies, containerization, RESTful APIs, and user interface development.
- Experience designing and maintaining scalable backend solutions using Python frameworks.
- Familiarity with relational database systems such as PostgreSQL and MySQL.
- Proficiency with Docker; Kubernetes experience is a plus.
- Proficiency with Git\-based version control and collaborative software development workflows.
- Hands\-on experience with full stack development and a strong understanding of the software development life cycle.
- Ability to work effectively both independently and collaboratively in a fast\-paced environment.
DESIRED SKILLS
- Experience developing modern front\-end applications using React.
- Hands\-on experience designing or developing LLM\-powered agentic workflows that use tool calling, structured outputs, task planning, and multi\-step execution.
- Familiarity with component\-based UI development, state management, and reusable front\-end design patterns.
- Experience integrating React\-based frontends with RESTful APIs or Python\-based backend services.
- Experience supporting AI, machine learning, data analytics, or mission\-focused software applications.
- Familiarity with cloud\-native application development, DevSecOps practices, or secure software delivery environments.
- Experience implementing reliable AI workflows with validation, error handling, retries, logging, and fallback strategies.
- Understanding of MLOps concepts, including model registry/versioning, reproducible environments, and CI/CD for ML\-enabled systems.
WHAT YOU’LL NEED TO SUCCEED
- Clearance: Current TS/SCI Clearance
- Education: Bachelor’s degree preferred
- Due to US Government Contract Requirements, only US Citizens are eligible for this role
- Role requirements: Hybrid work environment with 3 days on site in Fayetteville, NC and two days WFH
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, competitive pay and paid time off
- Community: Award\-winning culture of innovation and a military\-friendly workplace
OWN YOUR OPPORTUNITY
Explore a career at GDIT and you’ll find endless opportunities to grow alongside colleagues who share your passion for the mission and delivering results. \#iexjobs \#iexpriority
Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans
\#iexjobs
### 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%
Citizenship
U.S. Citizenship Required
### Salary and Benefit Information
The likely salary range for this position is $161,500 \- $218,500\. 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 $161K-$218K range is above the median for AI Software Engineer roles in our dataset (median: $183K across 194 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 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% 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 $219,250 based on 424 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($190K) sits 13% below the category median. Disclosed range: $161K to $218K.
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 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 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).
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 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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