Interested in this AI Software Engineer role at General Dynamics Information Technology?
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About This Role
Clearance Level
Top Secret SCI \+ Polygraph
Category
Data Science and Data Engineering
Location
Chantilly, Virginia
*(Onsite Workplace)*
Key Skills For Success
Chatbots
ElasticSearch
RAG Pipeline
##### REQ\#:RQ226271
##### 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
-------------------
Transform technology into opportunity as an AI Software Engineer (SME) with GDIT. We are seeking a hands\-on software engineer with expertise in designing, developing, and sustaining cloud‑enabled applications in Lean Agile environments to deliver secure, scalable, and maintainable mission‑critical customer systems. The role requires expertise in frontend and backend Web development and modern search engineering technologies, including Large Language Models (LLM), Retrieval‑Augmented Generation (RAG), and AI‑assisted retrieval system capabilities development.
KEY TASKS:
- Participate in all Lean Agile scrums, sprint planning, and grooming sessions.
- Consult and coordinate with stakeholders for problem resolution, task scheduling, resource needs, and task clarification.
- Work in partnership with an integrated team of staff and contractors.
- Coordinate and collaborate with security, operations, engineering, testing, and other teams to provide system information and technical support.
- Understand and work within approved cloud environments (e.g., AWS).
- Design, develop, and modify software systems.
- Unit test software and perform code reviews.
- Design and implement scalable search and retrieval systems leveraging Elasticsearch.
- Develop and integrate LLM‑powered capabilities, including Retrieval‑Augmented Generation (RAG) pipelines.
- Document and track vendor software roadmaps for software and patch version updates.
REQUIRED SKILLS AND DEMONSTRATED EXPERIENCE:
- Demonstrated experience working in Lean Agile Development environment.
- Demonstrated experience translating customer and system requirements into actionable design specifications.
- Demonstrated experience designing system interfaces, including defining integration points, data flows, and data exchange workflows.
- Demonstrated experience building or integrating LLM\-based solutions, including RAG pipelines, semantic search, and AI\-assisted retrieval systems.
- Demonstrated experience designing and implementing data pipelines for hybrid search (BM25 \+ vector search) in RAG workflows, covering ingestion, chunking, embeddings, and vector retrieval.
- Demonstrated experience with Elasticsearch, included index design and optimization, query development, relevance tuning, and cluster scaling.
- Demonstrated hands\-on experience in frontend Single Page Applications (SPA) development using React (hooks), HTML5, CSS3, and modern JavaScript best practices.
- Demonstrated hands\-on experience in backend development, including designing and building RESTful APIs with Node.js and Express.js (or comparable modern backend frameworks).
- Demonstrated hands\-on experience troubleshooting web protocols and server technologies, including Apache Tomcat, Node.js, Web Services, and SSL/TLS security configurations.
- Demonstrated experience using modern testing platforms such as Jest or Karma.
- Demonstrated experience with DevOps tools including Git, Jenkins, and Nexus.
HIGHLY DESIRED SKILLS AND DEMONSTRATED EXPERIENCE:
- Experience with the customer’s Lean Agile methodology.
- Experience evaluating, selecting, or integrating LLMs (e.g., OpenAI, open‑source models) into enterprise applications.
- Experience with vector databases, hybrid search (keyword \+ semantic), and relevance optimization.
- Experience with or exposure to RAG, LLM integrations, or semantic search.
- Experience with Elasticsearch or similar search platforms, with some exposure to optimization or scaling.
- Experience mentoring teams in search engineering and AI/LLM best practices, with a demonstrated ability to improve overall team capability.
- Experience with React \+ Node/Express development, with the ability to quickly ramp into search/AI domains.
- Experience working on APIs or systems that integrate with external AI/ML services.
- Demonstrated ability to learn quickly with guidance and become a team multiplier.
WHAT YOU’LL NEED TO SUCCEED:
- Education: Bachelor’s degree in Computer Science, Engineering, or a related technical discipline, or the equivalent combination of education, technical certifications or training, or work experience.
- Experience: 10\+ years
- Security clearance: TS/SCI with Polygraph
- Location: Chantilly, VA (On Customer Site)
- US Citizenship Required
\#WeAreGDIT
\#JET
\#GDITEnhanced2026
\#VA\_2026Alumni
### 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 $186,542 \- $252,379\. 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 $186K-$252K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $185K across 231 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 4,317 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 $218,500 based on 729 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. Disclosed range: $186K to $252K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 13 open AI roles right now. They're hiring across Data Scientist, Data Engineer, AI/ML Engineer, AI Software Engineer. Positions span Remote, US, Arlington, VA, US, Chantilly, VA, US. Compensation range: $154K - $287K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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