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About This Role
- Chicago, IL • Hybrid • Full\-Time
Department: Business Technology
About Hireology
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Hireology builds purpose\-built hiring software for healthcare, hospitality, and retail automotive. Our platform helps businesses attract, engage, and hire the right people, faster and with more confidence. We’re headquartered in Chicago and are a team of curious, practical people who care about doing good work.
About the Role
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Hireology’s Business Technology team builds the tools, workflows, and automations that the rest of the company runs on, from Finance and Revenue Operations to Sales, Customer Success, IT, and Implementation. As a Software Engineer, you’ll design, architect, and build software solutions that make the day\-to\-day work of those teams faster and less painful.
This isn’t a role where you sit behind a ticket queue. Business Technology is a new department, and you’ll be on the ground with non\-technical colleagues regularly, helping them figure out what they actually need and then building it. If you can hold a real conversation with a CFO or a CS manager and then go write the code, you’ll fit right in.
Hireology moves fast and makes decisions quickly. You’ll be context switching between teams, problems, and priorities on a regular basis, and that should energize you, not drain you. If you do your best work in a stable, predictable environment, this probably isn’t the right fit.
How We Build: AI\-First SDLC
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Hireology has adopted a full AI Software Development Lifecycle. Our engineers spend less time writing code by hand and more time on spec\-driven development, context engineering, and working alongside AI agents. We use Claude Code, the Superpowers framework, and the BMAD methodology, and while we lean on these tools heavily, we are very opinionated about the code that comes out the other side. Quality, maintainability, and good engineering judgment still matter, and it’s your job to make sure the output meets that bar. To be clear, this is not an invitation for vibe coders. We want real software engineering talent who genuinely knows how to build, but has made the shift to what modern engineering actually looks like.
In practice, this means you operate more like a manager than a solo developer. You’re directing agents, reviewing their output, correcting their course, and making judgment calls on what good looks like. The quality of what gets built depends heavily on how well you understand the problem in the first place, which means you have to lead with genuine customer empathy. You need to sit with people, understand how they actually work, and break down their problems in a way that’s clear enough for both humans and AI to act on.
Strong problem decomposition is at the core of this role. Before any code gets written, you should be able to articulate what the real problem is, why it matters, and what a good solution looks like. If you’ve moved beyond using AI just for autocomplete and are thinking seriously about what engineering looks like when agents do more of the work, this is the right place.
What You’ll Do
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- Work directly with internal teams across Finance, RevOps, Sales, CS, IT, and Implementation to understand their pain points and build solutions that actually fix them.
- Architect and build full\-stack applications in C\# .NET and Next.js, deployed in the cloud.
- Build and deploy AI agents, workflows, plugins, and skills to automate internal business processes.
- Work within Hireology’s AI SDLC using spec\-driven and context engineering practices (Claude Code, Superpowers, BMAD).
- Deeply understand the capabilities of third\-party systems we use across the business and build integrations and data flows that connect them in useful ways.
- Know when to reach for AI and when a simple, deterministic solution is the better call.
- Make technical ideas understandable to non\-technical stakeholders and collaborate with them through the design process.
- Practice continuous deployment, feature flagging, and test\-driven development.
- Keep the team in the loop by documenting decisions, sharing progress early, and communicating proactively.
- Own what you build long\-term. There’s no handoff to another team once something ships. You support it, maintain it, and improve it over time.
- Stay current on where AI engineering is headed and bring what you learn back to the team.
Core Competencies
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We hire against these competencies:
- Customer focus: your colleagues across Hireology are your customers, and their success is the measure of yours.
- Process innovation: a genuine interest in using technology and AI to make work better, not just faster.
- AI judgment: knowing when AI is the right tool and when it isn’t.
- Architect mindset: comfortable planning and designing before building, and leveraging AI agents and tools to do the implementation.
- Full\-stack depth: solid across front\-end, back\-end, and cloud infrastructure.
- Collaborative communication: able to translate technical work clearly for people who aren’t engineers.
- Autonomous ownership: can drive work independently and keeps everyone appropriately in the loop.
- Agility: comfortable context switching, making quick calls with incomplete information, and adjusting course without a lot of hand\-holding.
Required Qualifications
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- 5–7 years of full\-stack software engineering experience.
- Strong proficiency in C\# .NET and Next.js (React).
- Hands\-on experience in a cloud environment (AWS, Azure, GCP, or similar).
- Experience with AI tooling in an engineering context, such as LLM integrations, agentic workflows, or AI\-augmented development.
- Familiarity with continuous deployment pipelines and feature flagging (e.g., LaunchDarkly).
- Working knowledge of Test\-Driven Development (TDD).
- Comfortable evaluating third\-party systems, understanding their APIs and capabilities, and building integrations and data flows between them.
- Able to work independently and communicate proactively with technical and non\-technical stakeholders alike.
- Based in the Chicago area or willing to relocate; able to work on\-site at least 3 days per week for this role.
Preferred Qualifications
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- Microsoft Azure experience is a strong plus, as it is our primary cloud platform.
- Experience with agentic frameworks such as Claude Code, Superpowers, BMAD, or comparable tooling.
- Background supporting internal business systems across Finance, RevOps, CS, or similar functions.
- Familiarity with MCP (Model Context Protocol) servers, plugins, or skills ecosystems.
- Experience with prompt engineering, context engineering, or spec\-driven development practices.
Work Arrangement
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This role is hybrid based in Chicago, IL. Because Business Technology is a new department and much of the work involves face\-to\-face collaboration with non\-technical colleagues, we expect this person to be in office at least 3 days per week. That cadence may shift based on project needs. Other engineering roles at Hireology may have different arrangements.
Location: Chicago, IL (hybrid; minimum 3 days/week in office for this role, with flexibility based on business needs)
Compensation \& Benefits
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- Base salary: $120,000\- $140,000 (Plus bonus)
- Health, Dental, and Vision coverage from day one
- 401(k) with company match
- Unlimited PTO and mental health days
- External learning budget
- Real career advancement—we promote from within
Must be authorized to work in the United States. We are not able to provide Visa sponsorship. Agency and/or Third Party inquiries will not be accepted.
*Hireology is committed to equal employment opportunities regardless of race, color, genetic information, creed, religion, sex, sexual orientation, gender identity, national origin, age, marital status, disability status or protected veteran status, or any other category protected under the law. All employment decisions are solely based on business needs, job requirements, and individual qualifications. We support an inclusive workplace where Hireologists excel based on personal merit, qualifications, experience, ability, and job performance.*
Salary Context
This $120K-$140K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $190K across 219 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 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Hireology, 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. This role's midpoint ($130K) sits 44% below the category median. Disclosed range: $120K to $140K.
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.
Hireology AI Hiring
Hireology has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Chicago, IL, US. Compensation range: $140K - $140K.
Location Context
AI roles in Chicago pay a median of $201,225 across 312 tracked positions.
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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