Integrate security across the SSDLC, including code reviews, testing, and deployment. In this role ... and Generative AI solutions. You will operate hands-on across high-visibility initiatives ...
Integrate security across the SSDLC, including code reviews, testing, and deployment. In this role ... and Generative AI solutions. You will operate hands-on across high-visibility initiatives ...
Support data exploration, hypothesis testing, and foundational predictive analytics. Independently ... Experience with generative AI concepts, including large language models (LLMs), prompt engineering ...
Support data exploration, hypothesis testing, and foundational predictive analytics. Independently ... Experience with generative AI concepts, including large language models (LLMs), prompt engineering ...
Support data exploration, hypothesis testing, and foundational predictive analytics. Independently ... Experience with generative AI concepts, including large language models (LLMs), prompt engineering ...
Support data exploration, hypothesis testing, and foundational predictive analytics. Independently ... Experience with generative AI concepts, including large language models (LLMs), prompt engineering ...
Support data exploration, hypothesis testing, and foundational predictive analytics. Independently ... Experience with generative AI concepts, including large language models (LLMs), prompt engineering ...
Support data exploration, hypothesis testing, and foundational predictive analytics. Independently ... Experience with generative AI concepts, including large language models (LLMs), prompt engineering ...
... testing, and supporting deployment of a secure patient-facing interface. The Research Data ... Demonstrated experience working with large language models (LLMs) and/or generative AI systems.
... testing, and supporting deployment of a secure patient-facing interface. The Research Data ... Demonstrated experience working with large language models (LLMs) and/or generative AI systems.
Coordinate with business partners to conduct user acceptance testing. * Partner with the business ... experimentation with AI or generative tools (coursework, class projects, personal projects ...
Coordinate with business partners to conduct user acceptance testing. * Partner with the business ... experimentation with AI or generative tools (coursework, class projects, personal projects ...
Summer 2027 - Application Support Analyst Intern
Wichita, KS · On-site
$13.50 - $17.25/hr
Coordinate with business partners to conduct user acceptance testing * Recommend enhancements of ... with AI/ML tools or generative assistants (C opilot , LLMs, AI APIs) or a clear willingness to ...
Summer 2027 - Application Support Analyst Intern
Wichita, KS · On-site
$13.50 - $17.25/hr
Coordinate with business partners to conduct user acceptance testing * Recommend enhancements of ... with AI/ML tools or generative assistants (C opilot , LLMs, AI APIs) or a clear willingness to ...
Generative Ai Testing information
See Wichita, KS salary details
$28.60 - $32.22
6% of jobs
$32.22 - $35.83
4% of jobs
$35.83 - $39.45
13% of jobs
$39.90 is the 25th percentile. Wages below this are outliers.
$39.45 - $43.07
15% of jobs
The median wage is $46.53 / hr.
$43.07 - $46.69
13% of jobs
$46.69 - $50.30
24% of jobs
$50.53 is the 75th percentile. Wages above this are outliers.
$50.30 - $53.92
4% of jobs
$53.92 - $57.54
3% of jobs
$57.54 - $61.15
2% of jobs
$61.15 - $64.77
11% of jobs
$64.77 - $68.39
5% of jobs
$28
$48
$68
How much do generative ai testing jobs pay per hour?
As of Aug 27, 2026, the average hourly pay for generative ai testing in Wichita, KS is $48.06, according to ZipRecruiter salary data. Most workers in this role earn between $39.57 and $55.05 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?
| Aspect | Generative Ai Testing | Data Scientist |
|---|---|---|
| Required Credentials | Knowledge of AI models, testing tools, programming skills | Statistics, programming, data analysis certifications |
| Work Environment | AI development teams, testing labs, tech companies | Research labs, tech firms, finance, healthcare |
| Employer & Industry Usage | AI product testing, quality assurance in tech | Data 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 Wichita, KS?
For Generative Ai Testing jobs in Wichita, KS, the most frequently searched job titles are:
What job categories do people searching Generative Ai Testing jobs in Wichita, KS look for?
The top searched job categories for Generative Ai Testing jobs in Wichita, KS are:
Deloitte rating
8.2
Based on 93 frontline employees who took The Breakroom Quiz
46th of 152 rated financial services
Job description
As a Manager in AI Security Engineering, you will play a critical role in securing the development and deployment of AI/ML and Generative AI solutions. You will operate hands-on across high-visibility initiatives, embedding security, trust, and resilience into AI systems while enabling rapid, responsible innovation.
Recruiting for this role ends on 8/29/2026.
Work you'll do
You will bring strong engineering depth and applied AI knowledge, combined with expertise in cybersecurity principles, to design and deliver secure, scalable solutions. This role requires a collaborative, execution-focused leader who can influence teams, mentor engineers, and ensure AI systems meet enterprise security, risk, and compliance expectations.
Key Responsibilities
Secure, Outcome-Driven Delivery
Design and deliver AI-enabled solutions that are secure by design, balancing business value with risk mitigation. Solve complex problems while ensuring protection of data, models, and systems.
Hands-On AI Security Engineering
Actively contribute to architecture, design, and development of AI/ML and GenAI systems with embedded security controls. Integrate security across the SSDLC, including code reviews, testing, and deployment. In this role you will be responsible for managing AI defensive technologies and the operations of those technologies which will evolve over time.
AI Risk Identification and Mitigation
Identify and address AI-specific vulnerabilities, including prompt injection, data leakage, model manipulation, and misuse. Implement practical safeguards to ensure system integrity and trustworthiness.
Technical Leadership and Advocacy
Serve as a trusted technical voice for secure AI engineering. Ensure solutions are feasible, secure, and aligned with business and customer objectives.
Engineering Excellence with Security Focus
Maintain high standards for code quality, scalability, and security. Contribute to secure coding practices, reusable patterns, and continuous improvement across engineering teams.
Iterative and Responsible Innovation
Support rapid experimentation while applying appropriate security guardrails. Enable teams to innovate safely through controlled, risk-aware development practices.
Cross-Functional Collaboration
Partner closely with product, engineering, cybersecurity, and risk teams to embed AI security into solutions. Balance usability, performance, and security in decision-making.
Standards and Best Practices
Apply and help evolve standards for AI security, including data protection, access control, model validation, and monitoring within DevSecOps and MLOps pipelines.
Communication and Influence
Clearly articulate technical risks, trade-offs, and solutions to both technical and non-technical stakeholders. Contribute to alignment and informed decision-making.
Impact
This role directly contributes to reducing enterprise risk and strengthening trust in AI systems. Your work will enable secure adoption of AI capabilities while protecting critical assets and maintaining compliance.
The successful candidate would possess these skills
- Ability to work independently and collaborate as part of a team
- Effective written and verbal communication skills
- Meticulous attention to detail and quality of work product
- Ability to build and sustain professional relationships
- Ability to lead projects or workstreams
- Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
- Strong interpersonal skills and professional demeanor
- Ability to meet deadlines
- Ability to mentor and provide clear guidance to others
The team
Deloitte Technology US (DT - US) helps power Deloitte's success, which serves many of the world's largest, most respected organizations. We develop and deploy cutting-edge internal and go-to-market solutions that help Deloitte operate effectively and lead in the market. Our reputation is built on a tradition of delivering with excellence.
The ~3,000 professionals in DT - US deliver services including:
- Cyber Security
- Technology Support
- Technology & Infrastructure
- Applications
- Relationship Management
- Strategy & Communications
- Project Management
- Financials
Cyber Security
Cyber Security vigilantly protects Deloitte and client data. The team leads a strategic cyber risk program that adapts to a rapidly changing threat landscape, changes in business strategies, risks, and vulnerabilities. Using situational awareness, threat intelligence, and building a security culture across the organization, the team helps to protect the Deloitte brand.
Areas of focus include:
- Risk & Compliance
- Identity & Access Management
- Data Protection
- Cyber Design
- Incident Response
- Security Architecture
- Business Partnership
Qualifications
Required:
- Bachelor's degree or equivalent in Computer Science, Computer Engineering, Business Administration
- Minimum 6 years of relevant experience in software engineering, cybersecurity, and/or including AI/ML, with hands-on delivery experience
- Minimum 1 year of people and/or process management experience
Preferred:
- Strong understanding of AI/GenAI technologies and associated security risks (e.g., prompt injection, data exposure, adversarial threats)
- Experience building and securing applications using Python, JavaScript, or similar, along with ML frameworks (e.g., PyTorch, TensorFlow)
- Familiarity with secure development practices (DevSecOps) and integrating security into CI/CD and MLOps pipelines
- Experience with cloud platforms (AWS, Azure, GCP) and cloud-native security principles
- Knowledge of data protection, identity/access management, and secure architecture patterns
- Ability to work across teams, mentor engineers, and contribute to a strong engineering culture
- Strong communication skills with the ability to translate technical concepts into business-relevant insights
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $118,700 to $243,700.
You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.
EA_ExpHire
RITM10427821
As a Manager in AI Security Engineering, you will play a critical role in securing the development and deployment of AI/ML and Generative AI solutions. You will operate hands-on across high-visibility initiatives, embedding security, trust, and resilience into AI systems while enabling rapid, responsible innovation.
Recruiting for this role ends on 8/29/2026.
Work you'll do
You will bring strong engineering depth and applied AI knowledge, combined with expertise in cybersecurity principles, to design and deliver secure, scalable solutions. This role requires a collaborative, execution-focused leader who can influence teams, mentor engineers, and ensure AI systems meet enterprise security, risk, and compliance expectations.
Key Responsibilities
Secure, Outcome-Driven Delivery
Design and deliver AI-enabled solutions that are secure by design, balancing business value with risk mitigation. Solve complex problems while ensuring protection of data, models, and systems.
Hands-On AI Security Engineering
Actively contribute to architecture, design, and development of AI/ML and GenAI systems with embedded security controls. Integrate security across the SSDLC, including code reviews, testing, and deployment. In this role you will be responsible for managing AI defensive technologies and the operations of those technologies which will evolve over time.
AI Risk Identification and Mitigation
Identify and address AI-specific vulnerabilities, including prompt injection, data leakage, model manipulation, and misuse. Implement practical safeguards to ensure system integrity and trustworthiness.
Technical Leadership and Advocacy
Serve as a trusted technical voice for secure AI engineering. Ensure solutions are feasible, secure, and aligned with business and customer objectives.
Engineering Excellence with Security Focus
Maintain high standards for code quality, scalability, and security. Contribute to secure coding practices, reusable patterns, and continuous improvement across engineering teams.
Iterative and Responsible Innovation
Support rapid experimentation while applying appropriate security guardrails. Enable teams to innovate safely through controlled, risk-aware development practices.
Cross-Functional Collaboration
Partner closely with product, engineering, cybersecurity, and risk teams to embed AI security into solutions. Balance usability, performance, and security in decision-making.
Standards and Best Practices
Apply and help evolve standards for AI security, including data protection, access control, model validation, and monitoring within DevSecOps and MLOps pipelines.
Communication and Influence
Clearly articulate technical risks, trade-offs, and solutions to both technical and non-technical stakeholders. Contribute to alignment and informed decision-making.
Impact
This role directly contributes to reducing enterprise risk and strengthening trust in AI systems. Your work will enable secure adoption of AI capabilities while protecting critical assets and maintaining compliance.
The successful candidate would possess these skills
- Ability to work independently and collaborate as part of a team
- Effective written and verbal communication skills
- Meticulous attention to detail and quality of work product
- Ability to build and sustain professional relationships
- Ability to lead projects or workstreams
- Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
- Strong interpersonal skills and professional demeanor
- Ability to meet deadlines
- Ability to mentor and provide clear guidance to others
The team
Deloitte Technology US (DT - US) helps power Deloitte's success, which serves many of the world's largest, most respected organizations. We develop and deploy cutting-edge internal and go-to-market solutions that help Deloitte operate effectively and lead in the market. Our reputation is built on a tradition of delivering with excellence.
The ~3,000 professionals in DT - US deliver services including:
- Cyber Security
- Technology Support
- Technology & Infrastructure
- Applications
- Relationship Management
- Strategy & Communications
- Project Management
- Financials
Cyber Security
Cyber Security vigilantly protects Deloitte and client data. The team leads a strategic cyber risk program that adapts to a rapidly changing threat landscape, changes in business strategies, risks, and vulnerabilities. Using situational awareness, threat intelligence, and building a security culture across the organization, the team helps to protect the Deloitte brand.
Areas of focus include:
- Risk & Compliance
- Identity & Access Management
- Data Protection
- Cyber Design
- Incident Response
- Security Architecture
- Business Partnership
Qualifications
Required:
- Bachelor's degree or equivalent in Computer Science, Computer Engineering, Business Administration
- Minimum 6 years of relevant experience in software engineering, cybersecurity, and/or including AI/ML, with hands-on delivery experience
- Minimum 1 year of people and/or process management experience
Preferred:
- Strong understanding of AI/GenAI technologies and associated security risks (e.g., prompt injection, data exposure, adversarial threats)
- Experience building and securing applications using Python, JavaScript, or similar, along with ML frameworks (e.g., PyTorch, TensorFlow)
- Familiarity with secure development practices (DevSecOps) and integrating security into CI/CD and MLOps pipelines
- Experience with cloud platforms (AWS, Azure, GCP) and cloud-native security principles
- Knowledge of data protection, identity/access management, and secure architecture patterns
- Ability to work across teams, mentor engineers, and contribute to a strong engineering culture
- Strong communication skills with the ability to translate technical concepts into business-relevant insights
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. A...
About Deloitte
Sourced by ZipRecruiter
Industry
Finance and insurance and business management consulting
Company size
10,000+ Employees
Headquarters location
Orlando, FL, US