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About This Role
Overview:
FTI Defense is seeking an AI/ML Software Engineer to design, build, and deploy secure, scalable software and data systems that support mission operations, analytics, and simulation environments. This role is for a hands\-on engineer and someone who loves writing code, building systems end\-to\-end, and solving real\-world technical challenges in secure, distributed environments.
FTI Defense delivers mission\-focused solutions to the Department of Defense (DoD/DoW) and Intelligence Community (IC) through advanced engineering, digital transformation, and program execution expertise. We help our customers solve complex challenges by integrating people, process, and technology.
Responsibilities:
Software Design \& Development* Design and implement APIs, data pipelines, and simulation runtime logic that connect and enable mission applications.
- Develop software using modern programming languages such as Java, Python, C\+\+, or TypeScript/Angular.
- Write clean, testable, and maintainable code following secure coding and software engineering best practices.
- Build and integrate modular microservices to improve scalability, maintainability, and interoperability.
Cloud \& Containerized Environments* Build and deploy containerized, cloud\-native services using Docker, Kubernetes, and CI/CD pipelines (GitLab, Jenkins, or equivalent).
- Implement Infrastructure\-as\-Code and automation scripts to accelerate deployment and configuration management.
- Contribute to secure deployments across hybrid or disconnected environments (IL4–IL6, AWS GovCloud, or on\-prem).
Systems Integration \& Distributed Computing* Develop distributed systems and data integration frameworks using message buses such as Kafka or Redis.
- Engineer data flow between analytic, AI, and simulation components to support real\-time mission use cases.
- Collaborate with system engineers and architects to ensure interoperability across software ecosystems.
Data \& Analytics Integration* Build and manage databases (PostgreSQL, MongoDB, graph DBs) and model complex data relationships.
- Develop data services that feed analytics pipelines or integrate AI/ML outputs into runtime systems.
- Work with serialization and exchange formats such as JSON, Protobuf, GeoJSON, or KML.
Security, Testing \& Sustainment* Write, test, and deploy software within secure or classified environments.
- Automate testing and monitoring to ensure performance, reliability, and repeatable deployments.
- Support the transition of prototypes to operational systems, focusing on maintainability and observability.
Education/Qualifications:
Minimum Requirements:* Must be a U.S. citizen and be willing to obtain and maintain a security clearance, as needed.
- 6\-10\+ years of professional software engineering experience.
- 3\+ years of professional experience with DevSecOps, Zero\-Trust, or ATO/RMF processes in Department of Defense (DoD/DoW) environments.
- Strong full\-stack or systems engineering background.
- Proficiency in one or more of the following languages: Java, Python, C\+\+, or TypeScript/Angular.
- Experience building containerized, cloud\-native solutions using Docker, Kubernetes, and CI/CD pipelines.
- Complete understanding of distributed systems and message buses (Kafka, Redis, etc.).
- Experience developing or integrating analytics and AI models into production systems.
Preferred Qualifications:* Experience deploying code in IL4–IL6 or edge/disconnected environments.
- Familiarity with databases such as PostgreSQL, MongoDB, or graph databases.
- Knowledge of Infrastructure\-as\-Code (Terraform, CloudFormation, or CDK).
- Bachelor’s degree in Computer Science, Software Engineering, or a related technical field.
- Active Secret clearance preferred; ability to obtain one is required.
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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 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At FTI Defense, 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 $232,000 based on 797 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
FTI Defense AI Hiring
FTI Defense has 3 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Washington, DC, US, Chesapeake, VA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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