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About This Role
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Job Summary:
The AI Software Engineer I is responsible for supporting the development, implementation, and maintenance of software solutions and AI\-related initiatives within the organization. This position works collaboratively with internal teams to assist with technical projects, application support, system enhancements, and process improvements while gaining hands\-on experience in software engineering and artificial intelligence technologies.
Physical Requirements: Stand or Sit(Stationary position), Walk(Move, Traverse), Use hand/fingers to handle or feel (Operate, Activate, Use, Prepare, Inspect, Place, Detect, Position), Talk/hear(Communicate, Detect, Converse with, Discern, Convey, Express oneself, Exchange information), See (Detect, Determine, Perceive, Identify, Recognize, Judge, Observe, Inspect, Estimate, Assess), Reaching, Repetitive Motion Function in the Job: Sedentary Work\- Exerting up to 10 pounds of force occasionally, and/or a negligible amount of force frequently or constantly to lift, carry, push, pull or otherwise move objects, including the human body. Sedentary work involves sitting most of the time. Jobs are sedentary if walking and standing are required only occasionally, and all other sedentary criteria are met. Job Function:
- Support the development, testing, and maintenance of software applications and AI\-related solutions
- Assist with troubleshooting, debugging, and resolving technical issues
- Collaborate with team members on projects, enhancements, and process improvements
- Participate in system design discussions, documentation, and code reviews
- Learn and apply software development standards, best practices, and company procedures
- Support integration of applications, tools, and automation processes as needed
- Conduct research and assist with evaluation of new technologies and technical solutions
- Maintain accurate technical documentation and project updates
- Work effectively within a collaborative and team\-oriented environment
SUPPLEMENTAL DUTIES \& RESPONSIBILITIES
- Pursues training and development opportunities; Strives to continuously build knowledge and skills
- Perform other duties as assigned
Required Skills:
- Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Information Systems, or a related technical field required
- Basic understanding of software development principles and programming concepts
- Foundational knowledge of artificial intelligence, machine learning, automation, or data\-related technologies
- Basic understanding of AI large language models (LLMs) and modern AI tooling (e.g., Claude, Copilot Studio, OpenAI models), including their capabilities.
Demonstrated projects as part of college work in AI projects a huge plus.
- Exposure to or foundational understanding of agentic AI development concepts, including multi\-step reasoning, tool integration, and workflow orchestration using AI agents
- Familiarity with one or more programming languages such as Python, Java, JavaScript, or C\+\+
- Foundational knowledge of artificial intelligence, machine learning, automation, or data\-related technologies
- Strong analytical, troubleshooting, and problem\-solving skills
- Ability to learn new technologies and adapt in a fast\-paced environment
- Effective verbal and written communication skills
- Strong organizational skills and attention to detail
- Ability to work independently and collaboratively within a team environment
Reliance, Inc. IS A DRUG\-FREE WORK ENVIRONMENT EEO/DISABILITY/VETERAN
Compensation Range: The anticipated compensation for this position is USD $80,000\.00/Yr. \- USD $100,000\.00/Yr. depending on experience, qualifications, and location.
Salary Context
This $80K-$100K range is in the lower quartile 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 Reliance IT, Inc, 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. This role's midpoint ($90K) sits 59% below the category median. Disclosed range: $80K to $100K.
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.
Reliance IT, Inc AI Hiring
Reliance IT, Inc has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Chicago, IL, US. Compensation range: $100K - $100K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% below the national 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.
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