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Generative Ai Testing Jobs in Bolingbrook, IL (NOW HIRING)

iManage

Senior AI Engineer

Chicago, IL · On-site

$180K - $220K/yr

... our generative AI document assistant, as well as document classification, extraction, and LLM ... testing built in * Applying modern engineering practices for production AI systems, including ...

iManage

Senior AI Engineer

Chicago, IL · On-site

$180K - $220K/yr

... our generative AI document assistant, as well as document classification, extraction, and LLM ... testing built in * Applying modern engineering practices for production AI systems, including ...

James Hardie

Senior AI/ML Solutions Engineer

Chicago, IL · On-site

$57 - $73.50/hr

Design and implement solutions leveraging Generative AI (LLMs, text generation, RAG pipelines) and ... Establish and follow best practices for model deployment, versioning, testing, and observability.

Kirkland & Ellis LLP.

AI Innovation Advisor

Chicago, IL · On-site

... generative AI capabilities, limitations, and firm governance requirements. • Coordinate tasks, timelines, and stakeholders for AI workflow development initiatives. Workflow Development & Testing ...

U.S. Bank

Lead AI Engineer - Observability

Chicago, IL · On-site

$139 - $164/hr

Job Summary The Lead Engineer (Generative AI) is a senior technical role responsible for designing ... code reviews, testing, and documentation * 5. Technical Leadership & Influence Provide ...

New

US Bank

Lead AI Engineer - Observability

Chicago, IL · On-site

$105K - $139K/yr

Job Summary The Lead Engineer (Generative AI) is a senior technical role responsible for designing ... Build and manage CI/CD pipelines supporting automated testing, deployment, and release management

U.S. Bank

Lead AI Engineer - Observability

Chicago, IL · On-site

$105K - $139K/yr

Job Summary The Lead Engineer (Generative AI) is a senior technical role responsible for designing ... Build and manage CI/CD pipelines supporting automated testing, deployment, and release management

Us Bank

Lead AI Engineer - Observability

Chicago, IL · On-site

$139 - $164/hr

Job Summary The Lead Engineer (Generative AI) is a senior technical role responsible for designing ... Build and manage CI/CD pipelines supporting automated testing, deployment, and release management

New

Whiztek Corp

Lead GenAI Engineer

Chicago, IL · On-site

$105K - $139K/yr

Role: Lead Agentic AI/AI Engineer - Generative AI & Machine Learning Location:  Chicago, IL ... Familiar with functional and non-functional testing of AI/ML applications and operationalizing it ...

New

US Bank

Lead AI Platform Engineer

Chicago, IL · On-site

$105K - $139K/yr

Job Summary The Lead Engineer (Generative AI) is a senior technical role responsible for designing ... Build and manage CI/CD pipelines supporting automated testing, deployment, and release management

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Generative Ai Testing information

See Bolingbrook, IL salary details

$31

$53

$75

How much do generative ai testing jobs pay per hour?

As of Sep 6, 2026, the average hourly pay for generative ai testing in Bolingbrook, IL is $53.13, according to ZipRecruiter salary data. Most workers in this role earn between $43.75 and $60.87 per hour, depending on experience, location, and employer.

What is generative AI testing?

Generative AI Testing refers to the process of evaluating and validating AI systems, particularly those that generate content such as text, images, or code. This type of testing focuses on assessing the accuracy, reliability, fairness, and safety of generative models to ensure they function as intended and avoid producing harmful or biased outputs. Testers use various methods, including automated and manual techniques, to check for issues like hallucinations, inappropriate content, or security vulnerabilities. The goal is to build trust in generative AI systems and ensure they meet quality and ethical standards before deployment.

What are some common challenges faced when testing generative AI models, and how can I prepare to address them in this role?

Testing generative AI models often involves unique challenges such as evaluating the quality and relevance of generated content, detecting bias or inappropriate outputs, and ensuring model consistency across various prompts. You may work closely with data scientists and engineers to create robust evaluation frameworks and develop automated as well as manual testing strategies. Familiarity with prompt engineering, statistical evaluation techniques, and domain-specific knowledge will help you address these challenges effectively. Proactively staying updated on industry best practices and collaborating with cross-functional teams are key to success in this dynamic field.

What are the key skills and qualifications needed to thrive as a generative AI testing specialist, and why are they important?

To thrive as a Generative AI Testing Specialist, you need a robust understanding of machine learning principles, model evaluation techniques, and a background in computer science or a related field. Familiarity with tools such as Python, TensorFlow, PyTorch, and model evaluation frameworks, as well as experience with automated testing platforms, is typically required. Analytical thinking, attention to detail, and strong communication skills help you identify model weaknesses and collaborate effectively with development teams. These skills are crucial to ensure the reliability, safety, and ethical deployment of generative AI solutions.

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

AspectGenerative Ai TestingData Scientist
Required CredentialsKnowledge of AI models, testing tools, programming skillsStatistics, programming, data analysis certifications
Work EnvironmentAI development teams, testing labs, tech companiesResearch labs, tech firms, finance, healthcare
Employer & Industry UsageAI product testing, quality assurance in techData analysis, predictive modeling across industries

Generative Ai Testing focuses on evaluating and validating AI-generated content and models, ensuring quality and accuracy. Data Scientists analyze data, build models, and derive insights. While both roles require programming and AI knowledge, Generative Ai Testing emphasizes testing processes, whereas Data Scientists focus on data analysis and model development.

How do I become a Generative AI Testing?

To become a Generative AI Tester, develop skills in machine learning, natural language processing, and programming languages like Python. Gain experience with AI frameworks such as TensorFlow or PyTorch and understand data quality and model evaluation techniques. Relevant certifications and hands-on projects can enhance your qualifications for roles in AI testing environments.

Is Generative AI Testing a good career?

Generative AI Testing is a growing field within AI development, focusing on evaluating the quality and safety of AI-generated content. It requires skills in machine learning, programming, and understanding AI models, often involving tools like Python and TensorFlow. The role offers opportunities in tech companies and research labs, with demand expected to increase as AI applications expand.

What are popular job titles related to Generative Ai Testing jobs in Bolingbrook, IL?

For Generative Ai Testing jobs in Bolingbrook, IL, the most frequently searched job titles are:

What job categories do people searching Generative Ai Testing jobs in Bolingbrook, IL look for?

The top searched job categories for Generative Ai Testing jobs in Bolingbrook, IL are:

What cities near Bolingbrook, IL are hiring for Generative Ai Testing jobs?

Cities near Bolingbrook, IL with the most Generative Ai Testing job openings:

Generative Ai Testing jobs near you

AI SYSTEM INTEGRATION ENGINEER

Judge Group, Inc.

Chicago, IL • On-site

$80 - $100/hr

Other

Posted 20 days ago

Key responsibilities

  • Design, develop, test, deploy, and maintain integrations between AI solutions, enterprise applications, APIs, workflow platforms, and data sources.

  • Build reusable services, connectors, and integration frameworks that support multiple AI and digital coworker use cases.

  • Support Retrieval-Augmented Generation (RAG) and enterprise knowledge management solutions by integrating structured and unstructured data sources.

Job description

Location: Chicago, IL Salary: $80.00 USD Hourly - $100.00 USD Hourly Description: Title: AI System Integration Engineer
Job Summary
We are seeking a Senior AI Integration Engineer to design, develop, and support enterprise-grade integrations that connect AI-powered digital coworkers, Generative AI platforms, business applications, data sources, and workflow systems. The ideal candidate will bring strong expertise in cloud-native engineering, enterprise integrations, AI technologies, and production platform support.
This role will collaborate closely with Product, Engineering, Architecture, Security, Risk, and Business stakeholders to build scalable, secure, and reusable integration patterns that accelerate AI adoption across the organization.
Key Responsibilities
  • Design, develop, test, deploy, and maintain integrations between AI solutions, enterprise applications, APIs, workflow platforms, and data sources.
  • Build reusable services, connectors, and integration frameworks that support multiple AI and digital coworker use cases.
  • Develop and implement API-driven, event-driven, and microservices-based integration architectures.
  • Support Retrieval-Augmented Generation (RAG) and enterprise knowledge management solutions by integrating structured and unstructured data sources.
  • Configure workflow orchestration, permissions, tool access, and governance controls for AI agents and digital coworkers.
  • Collaborate with Product, Security, Risk, Architecture, and Application teams to validate technical feasibility, dependencies, access requirements, and implementation strategies.
  • Troubleshoot production issues, perform root cause analysis, and implement sustainable remediation solutions.
  • Contribute to sprint planning, backlog refinement, release management, dependency tracking, and project status reporting.
  • Create and maintain technical documentation, interface specifications, test plans, deployment guides, operational runbooks, and support materials.
  • Continuously improve integration standards, monitoring capabilities, security controls, and delivery processes.

Required Qualifications
  • 5+ years of experience in Software Engineering, Integration Engineering, Platform Engineering, Cloud Engineering, or related technology roles.
  • Proven experience designing and implementing enterprise-scale integrations using APIs, microservices, event-driven architectures, and service-based platforms.
  • Hands-on experience with cloud-native development and deployment, preferably on Microsoft Azure.
  • Strong experience supporting production platforms, including monitoring, troubleshooting, incident management, and root cause analysis.
  • Understanding of enterprise security principles including authentication, authorization, identity management, data protection, logging, and audit controls.
  • Experience translating business requirements into practical technical solutions and architecture designs.
  • Experience working within Agile delivery environments using tools such as Azure DevOps, Jira, ServiceNow, Confluence, and SharePoint.
  • Strong communication, stakeholder management, and technical documentation skills.

Preferred Qualifications
  • Experience working in financial services, regulated industries, enterprise governance, risk management, or AI compliance environments.
  • Working knowledge of:
    • Generative AI
    • Large Language Models (LLMs)
    • Retrieval-Augmented Generation (RAG)
    • AI Agents and Agentic Workflows
    • Prompt Engineering
    • Responsible AI and AI Governance
    • AI Observability and Model Monitoring
  • Experience with:
    • Azure OpenAI
    • Azure API Management
    • Azure Functions
    • Azure Kubernetes Service (AKS)
    • Microsoft Graph
    • Copilot Studio
    • LangChain
    • LangGraph
    • Model Context Protocol (MCP)
  • Experience integrating enterprise platforms such as SharePoint, ServiceNow, Snowflake, Databricks, Microsoft Fabric, document repositories, and operational systems.
  • Experience creating reusable connector frameworks, deployment documentation, test strategies, release notes, and operational runbooks.

Required Technical Skills
AI & Agentic Platforms
  • Generative AI and LLM concepts
  • AI Agents and Agentic Frameworks
  • RAG and Hybrid RAG architectures
  • Prompt Engineering
  • MCP (Model Context Protocol)
  • Responsible AI and Governance Controls
  • AI Observability, Monitoring, and Telemetry
  • LangChain and LangGraph

Integration Engineering
  • REST APIs
  • Service-Oriented Architecture
  • Event-Driven Architecture
  • Microservices Design
  • API Gateway Management
  • Authentication and Authorization Patterns
  • Integration Testing
  • Error Handling and Retry Mechanisms
  • Reusable Connector Development

Cloud & DevOps
  • Microsoft Azure
  • Azure Functions
  • Azure API Management (APIM)
  • Azure Kubernetes Service (AKS)
  • Azure DevOps
  • CI/CD Pipelines
  • Infrastructure-as-Code Concepts
  • Monitoring and Logging
  • Release and Environment Management

Data & Analytics
  • Python
  • SQL
  • Kafka
  • Snowflake
  • Databricks
  • Microsoft Fabric
  • Structured and Unstructured Data Integration
  • Knowledge Repository Management
  • Data Access Controls
  • Retrieval Optimization

Tools & Reporting
  • Azure DevOps
  • Jira
  • ServiceNow
  • Confluence
  • SharePoint
  • Power BI
  • Excel
  • Technical Documentation Platforms
  • Operational Runbooks and Dashboards

Enterprise Delivery
  • Cross-functional Collaboration
  • Dependency Management
  • Security and Risk Review Support
  • Governance Documentation
  • Vendor Coordination
  • Production Support Readiness
  • Release Planning and Change Management

Success Measures
  • Delivery of secure, scalable, and well-documented enterprise integrations.
  • Development of reusable integration and connector frameworks that improve engineering efficiency.
  • Faster onboarding of AI solutions and digital coworkers into enterprise systems and data platforms.
  • Reduction in production defects and improvement in incident response readiness.
  • High-quality technical documentation, operational support assets, and governance compliance artifacts.

Technical Skills (Mandatory)
  • LangChain / LangGraph Tool Stack
  • Python
  • Kafka
  • Snowflake
  • Databricks
  • Azure Cloud Services
  • REST APIs & Integration Engineering
  • Generative AI & RAG Frameworks
  • MCP and AI Agent Frameworks
  • Azure DevOps & CI/CD Pipelines

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Contact:
This job and many more are available through The Judge Group. Please apply with us today!

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