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Generative Ai Developer Jobs in Illinois (NOW HIRING)

Tror AI for everyone

Senior AI Developer

Mettawa, IL · On-site

$62.50 - $82.50/hr

Senior AI Developer Location: Mettawa, IL (Onsite) Design, build, and deploy cutting-edge AI ... and generative models. You will be instrumental in developing high-impact applications, with a ...

Motion Recruitment Partners, LLC

Senior AI Engineer

Chicago, IL · On-site

$107K - $147K/yr

Join an innovative organization that is investing heavily in Artificial Intelligence, Generative AI ... We are seeking a Senior AI Engineer to design, develop, and deploy intelligent solutions leveraging ...

Motion Recruitment

Senior AI Engineer

Chicago, IL

$107K - $147K/yr

Join an innovative organization that is investing heavily in Artificial Intelligence, Generative AI ... We are seeking a Senior AI Engineer to design, develop, and deploy intelligent solutions leveraging ...

Vizient

Senior AI Financial Operations Analyst

Chicago, IL · On-site

$88K - $109K/yr

Provide visibility into enterprise AI tooling adoption, including developer productivity tools, generative AI platforms, and AI orchestration solutions, while identifying and mitigating unsanctioned ...

Socket.dev

AI Strategy Lead

Chicago, IL · On-site

$192 - $278/hr

Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or ... Experience architecting scalable enterprise AI solutions and generative AI pipelines (e.g., Large ...

New

EVERSANA INTOUCH

Sr. AI Developer, Engineering

Chicago, IL

$56.25 - $74.25/hr

The ideal candidate possesses deep expertise in modern software engineering, cloud-native architecture, enterprise integrations, and generative AI technologies, along with the communication skills ...

Define Ventures

Sr. AI Developer, Engineering

Chicago, IL · On-site

$150 - $210/hr

The Senior AI Agency Engineer designs, builds, and deploys enterprise-grade AI solutions that connect generative AI, workflow automation, compliance controls, and business systems to accelerate ...

EVERSANA

Sr. AI Developer, Engineering

Chicago, IL · On-site

$56.25 - $74.25/hr

The Senior AI Agency Engineer designs, builds, and deploys enterprise-grade AI solutions that connect generative AI, workflow automation, compliance controls, and business systems to accelerate ...

iManage

Senior AI Engineer

Chicago, IL · On-site

$180K - $220K/yr

... our generative AI document assistant, as well as document classification, extraction, and LLM ... Applying modern engineering practices for production AI systems, including containerized services ...

iManage

Senior AI Engineer

Chicago, IL · On-site

$180K - $220K/yr

... our generative AI document assistant, as well as document classification, extraction, and LLM ... Applying modern engineering practices for production AI systems, including containerized services ...

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All JobsGenerative Ai Developer JobsGenerative Ai Developer Jobs in Illinois

Generative Ai Developer information

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How much do generative ai developer jobs pay per hour?

As of Sep 3, 2026, the average hourly pay for generative ai developer in Illinois is $43.89, according to ZipRecruiter salary data. Most workers in this role earn between $22.84 and $53.12 per hour, depending on experience, location, and employer.

What is a generative AI developer?

A Generative AI Developer is a technology professional who specializes in designing, building, and deploying artificial intelligence systems that can create new content, such as text, images, audio, or code. They work with advanced machine learning models, like generative adversarial networks (GANs) or large language models, to enable computers to produce original outputs. These developers often collaborate with data scientists, researchers, and product teams to integrate AI-generated content into software applications and business solutions.

What are the key skills and qualifications needed to thrive as a generative AI developer?

To thrive as a Generative AI Developer, you need strong programming skills (especially in Python), a deep understanding of machine learning concepts, and an advanced degree in computer science or a related field. Familiarity with frameworks like TensorFlow, PyTorch, and experience with cloud platforms or model deployment tools are typically required. Creative problem-solving, adaptability, and effective collaboration are standout soft skills in this evolving field. These abilities are crucial to design, implement, and refine generative models that solve real-world problems and drive innovation.

What are some common challenges faced by generative AI developers when deploying models in production environments?

Generative AI Developers often encounter challenges such as ensuring model reliability, managing computational resource requirements, and addressing ethical considerations like data bias or content safety. Deploying generative models at scale requires robust monitoring to detect unexpected outputs or model drift, and collaboration with data engineers and product teams to optimize performance. Staying up-to-date with evolving frameworks and best practices is essential, as production environments demand both technical rigor and adaptability to new AI advancements.

What is the difference between Generative Ai Developer vs Machine Learning Engineer?

AspectGenerative Ai DeveloperMachine Learning Engineer
CredentialsBachelor's or higher in CS, AI, or related fields; experience with deep learning frameworksBachelor's or higher in CS, Data Science, or related fields; strong programming skills
Work EnvironmentDevelops AI models for content creation, chatbots, and creative applicationsBuilds and deploys ML models for various data-driven solutions across industries
Industry UsageTech, entertainment, marketing, and creative sectorsFinance, healthcare, tech, and e-commerce sectors

While both roles involve AI and machine learning, Generative Ai Developers focus on creating models that generate content, such as images or text, whereas Machine Learning Engineers develop broader ML solutions for diverse applications. The roles often overlap but differ mainly in their specific focus areas and use cases.

How to become a generative AI developer?

To become a generative AI developer, you should have a strong foundation in programming languages like Python, experience with machine learning frameworks such as TensorFlow or PyTorch, and knowledge of neural network architectures like transformers. Gaining expertise in natural language processing and deep learning, along with practical experience through projects or internships, is essential. Certifications in AI or data science can also enhance your qualifications.

What are popular job titles related to Generative Ai Developer jobs in Illinois?

For Generative Ai Developer jobs in Illinois, the most frequently searched job titles are:

What job categories do people searching Generative Ai Developer jobs in Illinois look for?

The top searched job categories for Generative Ai Developer jobs in Illinois are:

What cities in Illinois are hiring for Generative Ai Developer jobs?

Cities in Illinois with the most Generative Ai Developer job openings:

Generative Ai Developer jobs near you
Infographic showing various Generative Ai Developer job openings in Illinois as of August 2026, with employment types broken down into 72% Full Time, 26% Part Time, and 2% Contract. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution, with an average salary of $91,282 per year, or $43.9 per hour.

Lead Engineer (Generative AI)

Amtex Enterprises Inc

Chicago, IL • On-site

$150 - $190/hr

Other

Posted 14 days ago

Job description

Job Title : Lead Engineer (Generative AI)

Duration: 6-12 plus months

Location
  • Minneapolis/St. Paul “Twin Cities,” MN
  • Bay Area, CA – San Francisco and surrounding areas
  • Charlotte, NC
  • Chicago, IL
Job Description

Job Summary

The Lead Engineer (Generative AI) is a senior technical role responsible for designing, developing, and operationalizing enterprise-scale Generative AI (GenAI) solutions. This position combines deep hands-on expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI architectures with strong technical leadership to deliver secure, scalable, and resilient AI systems.

The role partners across engineering, product, and business teams to translate complex requirements into production-ready AI capabilities aligned with enterprise standards for security, risk, and responsible AI.

Key Responsibilities
  1. GenAI Solution Engineering
    • Design, develop, and deploy GenAI solutions leveraging:
      • Large Language Models (LLMs)
      • Retrieval-Augmented Generation (RAG) architectures
      • Prompt engineering techniques
      • Agentic AI workflows and orchestration
    • Build intelligent systems using frameworks such as LangChain, LangGraph, AWS Bedrock, and Microsoft Foundry Agent Service
    • Evaluate emerging tools and frameworks to continuously improve solution quality and innovation
  2. GenAIOps & Lifecycle Management
    • Lead the end-to-end lifecycle of GenAI solutions, including:
      • Solution architecture and engineering
      • Integration with enterprise systems
      • Secure deployment and release management
      • Monitoring, observability, and continuous optimization
    • Implement GenAIOps best practices to ensure scalability, reliability, and cost efficiency
    • Establish logging, evaluation, and feedback mechanisms for production AI systems
  3. Cloud, Platform & Scalability Engineering
    • Architect and deploy GenAI applications across cloud environments (Azure and AWS)
    • Design distributed systems capable of supporting high-throughput, low-latency AI workloads
    • Leverage modern infrastructure practices:
      • Containerization (Docker)
      • Orchestration (Kubernetes)
      • Infrastructure as Code (Terraform, ARM/Bicep)
    • Ensure high availability, performance, and enterprise-grade security
  4. Software Engineering & Architecture
    • Develop scalable, maintainable applications using Python and microservices-based architectures
    • Apply secure coding standards and robust data handling practices for regulated environments
    • Build and manage CI/CD pipelines supporting automated testing, deployment, and release management
    • Enforce engineering best practices including code reviews, testing, and documentation
  5. Technical Leadership & Influence
    • Provide architectural leadership and guidance across GenAI initiatives
    • Drive critical design decisions for large-scale, complex AI solutions
    • Mentor and coach senior engineers and development teams
    • Translate business requirements into scalable, secure, and resilient technical solutions
    • Partner with stakeholders across product, business, risk, and security functions
Basic Qualifications
  • Bachelor’s degree, or equivalent work experience
  • Six to eight years of relevant experience
Experience Should Include
  • Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
  • 8+ years of experience in software engineering, platform engineering, or AI/ML solutions
  • 2+ years hands-on experience with GenAI technologies, including LLMs and RAG architectures and vector databases
  • Strong knowledge of agentic AI concepts and frameworks (e.g., LangChain, LangGraph)
  • Experience with cloud platforms (Azure and/or AWS)
  • Deep understanding of distributed systems and scalable architecture patterns
  • Proficiency in Python and microservices-based development
  • Experience with Docker, Kubernetes, and Infrastructure as Code tools
  • Demonstrated technical leadership and mentoring experience
Preferred Qualifications
  • Experience implementing GenAI solutions in enterprise or regulated environments
  • Familiarity with observability frameworks and AI lifecycle tooling
  • Understanding of AI governance, security, and compliance requirements
  • Experience contributing to or working with AI/ML or GenAI frameworks
  • Background in financial services or other highly regulated industries
Core Competencies Technical Depth & Innovation
  • Strong expertise in GenAI architectures and evolving AI technologies
  • Ability to balance experimentation with enterprise-grade reliability
Architecture & Systems Thinking
  • Designs scalable, distributed, and resilient systems
  • Aligns architecture decisions with enterprise standards and long-term strategy
Execution & Operational Excellence
  • Drives end-to-end delivery from concept through production
  • Ensures high standards for quality, security, and performance
Leadership & Collaboration
  • Influences without authority and leads through technical expertise
  • Mentors engineers and elevates overall team capability
Business & Stakeholder Alignment
  • Translates complex technical concepts into business outcomes
  • Partners effectively across product, engineering, and leadership teams
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