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Generative Ai Analyst Jobs in Chicago, IL (NOW HIRING)

TekCommands Inc

AWS + Generative AI Engineer

Chicago, IL · On-site

$66.75 - $87.50/hr

Develop scalable data pipelines for analytics and AI workloads. * Configure and manage AWS Config ... Mentor engineering teams on AWS and Generative AI technologies. Required Skills * 12-16 years of ...

Vizient

Senior AI Financial Operations Analyst

Chicago, IL · On-site

$88K - $109K/yr

Track, analyze, and optimize AI consumption metrics including token utilization, inference costs ... Experience working with public cloud AI services, generative AI platforms, and cloud-based ...

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All JobsGenerative Ai Analyst JobsGenerative Ai Analyst Jobs in Chicago, IL

Generative Ai Analyst information

See Chicago, IL salary details

$50.5K

$91.3K

$127.3K

How much do generative ai analyst jobs pay per year?

As of Sep 4, 2026, the average yearly pay for generative ai analyst in Chicago, IL is $91,310.00, according to ZipRecruiter salary data. Most workers in this role earn between $66,000.00 and $102,600.00 per year, depending on experience, location, and employer.

What is a generative AI analyst?

A Generative AI Analyst is a professional who specializes in analyzing, designing, and optimizing systems that use generative artificial intelligence models, such as large language models or image generators. Their work involves understanding how these AI models are developed, deployed, and utilized across various applications. They assess data quality, monitor model outputs, evaluate performance, and help improve the effectiveness and ethical use of generative AI technologies. Generative AI Analysts may also provide insights to organizations on best practices, risk management, and innovation opportunities related to AI. Their expertise bridges the gap between data science, AI development, and business strategy.

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

To thrive as a Generative AI Analyst, you need a solid background in data science, machine learning, and statistics, often supported by a degree in computer science or a related field. Familiarity with tools and frameworks such as Python, TensorFlow, PyTorch, and experience with large language models or generative adversarial networks (GANs) is typically required. Strong analytical thinking, creativity, and effective communication skills help you interpret complex data and present insights to stakeholders. These skills and qualities are crucial for developing innovative AI solutions, solving business challenges, and driving impactful results.

How does a generative AI analyst typically collaborate with data scientists and engineering teams?

A Generative AI Analyst frequently works alongside data scientists and engineering teams to interpret model outputs, assess data quality, and help translate business objectives into technical requirements. Collaboration usually involves regular meetings to review model performance, troubleshoot issues, and refine algorithms based on real-world feedback. Effective communication and a shared understanding of both AI concepts and business goals are essential, as the analyst often serves as a bridge between technical teams and stakeholders. This collaborative environment fosters continuous learning and innovation, making teamwork a core aspect of the role.

What is the difference between Generative Ai Analyst vs Data Scientist?

AspectGenerative Ai AnalystData Scientist
Required CredentialsBachelor's in CS, AI, or related fields; certifications in AI/MLBachelor's/Master's in CS, Statistics, or related fields; advanced certifications
Work EnvironmentTech companies, AI startups, research labsTech firms, finance, healthcare, consulting
Employer & Industry UsageFocus on developing and refining generative AI modelsAnalyze data, build predictive models, derive insights
Common Search & Comparison IntentUnderstanding roles in AI developmentData analysis and modeling skills

While both roles require strong technical skills and knowledge of AI and data analysis, a Generative Ai Analyst specializes in creating and optimizing generative AI models, whereas a Data Scientist focuses on analyzing data to inform business decisions. The roles often overlap but differ in their primary focus and application within organizations.

How much do generative AI analysts make?

Generative AI analysts typically earn between $70,000 and $130,000 annually, depending on experience, location, and industry. Senior roles or those with specialized skills in machine learning and deep learning can command higher salaries, often exceeding $150,000.

Is Generative AI a promising career?

Generative AI is a rapidly growing field with increasing demand for specialists such as Generative AI Analysts, who develop and refine AI models like GPT and DALL·E. Careers in this area often require skills in machine learning, programming, and data analysis, and offer opportunities across technology, healthcare, entertainment, and other industries. The field is expected to continue expanding as AI applications become more integrated into various sectors.

What are popular job titles related to Generative Ai Analyst jobs in Chicago, IL?

For Generative Ai Analyst jobs in Chicago, IL, the most frequently searched job titles are:

What job categories do people searching Generative Ai Analyst jobs in Chicago, IL look for?

The top searched job categories for Generative Ai Analyst jobs in Chicago, IL are:

Generative Ai Analyst jobs near you
Infographic showing various Generative Ai Analyst job openings in Chicago, IL as of August 2026, with employment types broken down into 100% Full Time. Highlights an 49% In-person, and 51% Hybrid job distribution, with an average salary of $91,310 per year, or $43.9 per hour.

AWS + Generative AI Engineer

TekCommands Inc

Chicago, IL • On-site

$66.75 - $87.50/hr

Contractor

Re-posted 21 days ago

Job description

We are looking for an experienced AWS + Generative AI Engineer to design and develop secure, scalable, and AI-powered cloud solutions on AWS. The ideal candidate will have expertise in AWS Machine Learning, AWS Bedrock, Data Pipelines, AWS Config, and cloud-native architectures. This role involves building enterprise AI solutions, developing data pipelines, and collaborating with cross-functional teams to deliver innovative cloud applications.

Top 3 Required Skills

  1. AWS Cloud & Machine Learning
  2. AWS Bedrock & Generative AI
  3. AWS Data Pipelines & Infrastructure Automation

Key Responsibilities

  • Design and develop cloud-native applications using AWS services.
  • Build and deploy Generative AI solutions using AWS Bedrock.
  • Design and implement machine learning workflows, model deployment, and monitoring.
  • Develop scalable data pipelines for analytics and AI workloads.
  • Configure and manage AWS Config and governance policies to ensure security and compliance.
  • Design secure, reliable, and cost-effective AWS architectures.
  • Collaborate with data engineers, data scientists, and application teams to deliver AI-powered solutions.
  • Implement Infrastructure as Code (IaC) and cloud automation.
  • Optimize AWS environments for performance, scalability, and cost.
  • Document architecture, technical designs, and best practices.
  • Mentor engineering teams on AWS and Generative AI technologies.

Required Skills

  • 12–16 years of experience in Cloud Engineering, Solution Architecture, or Enterprise Architecture.
  • Strong hands-on experience with Amazon Web Services (AWS).
  • Experience with:
    • AWS Bedrock
    • AWS Machine Learning Services
    • AWS Data Pipeline or similar orchestration tools
    • AWS Config
  • Strong knowledge of Generative AI and Large Language Models (LLMs).
  • Experience designing cloud-native and distributed applications.
  • Experience with Infrastructure as Code (Terraform or CloudFormation).
  • Strong understanding of cloud security, governance, and identity management.
  • Excellent communication and stakeholder management skills.

Preferred Skills

  • Experience with AWS-based commerce platforms.
  • Knowledge of AI/ML model deployment and lifecycle management.
  • Experience with cloud automation and DevOps practices.
  • Experience with Python or other scripting languages.

Certifications

  • AWS Certified Solutions Architect – Professional or
  • AWS Certified Machine Learning – Specialty or equivalent AWS certification