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About This Role
It's fun to work in a company where people truly BELIEVE in what they're doing!
Fullsteam is a leading provider of vertical software and embedded payments technology dedicated to helping businesses flourish by providing their customers with seamless experiences. With a dynamic and growing team of over 1,900 employees, we are committed to driving innovation and delivering best-in-class software and payment solutions that empower small and medium-sized businesses across numerous industries. Our purpose is to help our customers grow their businesses and delight their customers. Join us and be a part of a forward-thinking company that values growth, excellence, and the success of our clients.
We’re building a pipeline of exceptional AI Engineering talent for future opportunities within our organization. While this is not an immediate opening, we anticipate significant hiring in this space throughout the year.
As part of our broader AI strategy, we are investing heavily in Generative AI, Agentic Systems, and Ambient Intelligence to transform how we build products, operate internally, and serve our customers. These initiatives will drive expanded product capabilities, productivity gains, enhanced customer experiences, and measurable improvements in customer satisfaction (NPS).
If you're excited about shaping the next generation of intelligent systems, we encourage you to apply and connect with us for upcoming roles.
Primary Responsibilities
- Build production software – Design and implement scalable backend services, APIs, and data pipelines with a focus on reliability, performance, and maintainability
- Develop agentic systems – Create autonomous agents that leverage LLMs to reason, use tools, and orchestrate complex multi-step workflows
- Architect for scale – Design systems that handle real-time AI inference, conversation state, and tool execution with proper error handling and recovery
- Design, develop, and deploy machine learning models that drive product features, improve user engagement, and enhance decision-making processes
- Collaborate cross-functionally – Partner with Product, Design, and Engineering teams to define requirements and deliver AI-powered features.
- Maintain engineering excellence – Write clean, well-tested code; conduct thorough code reviews; champion best practices across the team
- Stay current – Evaluate emerging agentic frameworks, patterns, and tools; prototype new approaches to solve real problems
Skills & Competencies
Software Engineering (Core)
- Strong system design, API design, and distributed systems knowledge
- Clean code practices and testable architecture
- Experience with concurrency, async programming, and performance optimization
- Database experience (relational + vector stores)
- CI/CD, observability, and production operations
Agentic Systems
- Agent architecture (planning, orchestration, memory, tool use)
- LLM integration (prompting, function calling, context management)
- RAG pipelines (embeddings, vector search, retrieval strategies)
- Reliability patterns for non-deterministic systems
Machine Learning (ML)
- Understanding of training, inference, evaluation, and model tradeoffs
- Familiarity with embeddings and vector representations
- Exposure to frameworks such as PyTorch, Hugging Face, or scikit-learn
- Experience adapting or fine-tuning models is a plus
- Knowledge of evaluation metrics (precision/recall, F1, perplexity)
Nice to Have
- Experience with agent orchestration frameworks (e.g., LangGraph, AutoGen)
- Background in vector databases and semantic search
- Familiarity with AI safety and guardrail patterns
- Experience mentoring engineers or leading initiatives
Typical Qualifications for Consideration
- Bachelor’s degree in Computer Science, Engineering, or related field
- 6+ years of software engineering experience
- 2+ years building applications leveraging LLMs or agentic systems
- Proficiency in Python and modern frameworks (FastAPI, async/await)
- Experience with cloud platforms (AWS preferred: Lambda, Bedrock, OpenSearch)
- Proven ability to ship and operate production systems
- Strong problem-solving and communication skills
Additional Information
This is an evergreen requisition designed to build our talent network for upcoming AI Engineering roles. If your background aligns, a member of our team may reach out to you as opportunities arise that match your experience and interests.
Fullsteam supports an inclusive workplace that values diversity of thought, experience, and background. Fullsteam is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, ancestry, age, physical or mental disability, sex, sexual orientation, gender identity/expression, pregnancy, veteran status, marital status, creed, status with regard to public assistance, genetic status or any other status protected by federal, state, or local law.
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 8,943 AI roles we're tracking, AI Software Engineer positions make up 4% of the market. At IQware, 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. Mid-level AI roles across all categories have a median of $193,200.
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.
IQware AI Hiring
IQware has 1 open AI role right now. They're hiring across AI Software Engineer. Based in GA, US.
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 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 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).
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 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.
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