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Generative Ai Testing Jobs in Riverside, CA (NOW HIRING)

Capgroup

Senior Machine Learning Engineer

Irvine, CA · On-site

$112K - $154K/yr

You will join ahigh-performingteam of ML engineers and software engineers building Generative AI ... Maintain a high bar for code quality, testing, clarity, and reliability. * Contribute to ...

Rivian

Staff Security Engineer - AI Security

Irvine, CA

$140K - $175K/yr

Automate adversarial testing: Build AI-driven frameworks to scale our Red Teaming and vulnerability ... Deep understanding of generative AI systems, including RAG pipelines, large language models, vector ...

Pwc

AI Engineer

Irvine, CA

$50K - $112K/yr

... Applying generative AI techniques, including prompt engineering, LLM evaluation, and fine-tuning ... testing, and adversarial benchmarking, to assess reasoning, tool-calling reliability, and output ...

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Scadea

Sr. Product Manager

Irvine, CA · On-site

$135K - $179K/yr

... generative AI use cases for advisors, clients, operations, and service teams Use AI tools for discovery, requirements, analysis, prototyping, testing, and documentation Experience with rapid ...

APTIM

Digital Marketing Specialist

Irvine, CA · On-site

Candidates should also bring practical experience using generative AI tools to support research ... Support A/B and multivariate testing initiatives across email, landing pages, and campaign assets.

Pwc

US Tech - AI Engineering Senior Associate

Irvine, CA

$55K - $187K/yr

... and validate Generative AI agents and data pipelines, promoting reliability, scalability, and ... Responsibilities - Apply automated testing and governance controls effectively - Analyze complex ...

Esri

Sr. Web Strategist, Digital Experience

Redlands, CA

$82K - $95K/yr

... generative AI surfaces, and structured content reuse * Collaborate closely with SEO, Conversion ... Keep abreast of usability research, A/B testing, and quarterly performance reports to inform ...

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All JobsGenerative Ai Testing JobsGenerative Ai Testing Jobs in Riverside, CA

Generative Ai Testing information

See Riverside, CA salary details

$33

$56

$79

How much do generative ai testing jobs pay per hour?

As of Jul 30, 2026, the average hourly pay for generative ai testing in Riverside, CA is $56.05, according to ZipRecruiter salary data. Most workers in this role earn between $46.15 and $64.18 per hour, depending on experience, location, and employer.

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 much do AI testers get paid?

AI testers, involved in evaluating and validating generative AI models, typically earn salaries ranging from $60,000 to $120,000 annually depending on experience, location, and company size. Entry-level positions may start lower, while experienced testers with specialized skills in machine learning and data analysis can earn higher wages.

Is AI testing a good career?

AI testing, including roles like Generative AI Testing, is a growing field with increasing demand for skills in machine learning, data analysis, and software quality assurance. It offers opportunities in tech companies, research labs, and startups, often requiring knowledge of AI frameworks and testing tools. The career can be stable and rewarding for those with technical expertise and an interest in AI development.

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 salary of generative AI tester?

The salary of a generative AI tester typically ranges from $70,000 to $120,000 annually, depending on experience, location, and company size. Entry-level positions may start lower, while experienced testers with specialized skills in AI and machine learning can earn higher salaries. Certifications in AI or related fields can also influence compensation.

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.

How do I become an AI tester?

To become an AI tester, you should have a strong understanding of machine learning concepts, programming skills in languages like Python, and experience with data annotation and model evaluation. Familiarity with AI tools, testing frameworks, and quality assurance processes is also important. Gaining relevant certifications or training in AI and software testing can enhance your qualifications.

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 popular job titles related to Generative Ai Testing jobs in Riverside, CA? For Generative Ai Testing jobs in Riverside, CA, the most frequently searched job titles are:
What job categories do people searching Generative Ai Testing jobs in Riverside, CA look for? The top searched job categories for Generative Ai Testing jobs in Riverside, CA are:
What cities near Riverside, CA are hiring for Generative Ai Testing jobs? Cities near Riverside, CA with the most Generative Ai Testing job openings:
Generative Ai Testing jobs near you

Generative AI Senior Developer - Google Cloud (Contract to Hire) - Hybrid Remote - Western States...

Entisys Solutions Inc

Irvine, CA • Remote

$80 - $90/hr

Other

This job post has expired today. Applications are no longer accepted.

Job description

Description

Rate: $80 - $90/ hour (Depending on Experience) 



Note: MUST be US Citizen or Green Card Holder 



***NO RECRUITING AGENCIES***

***NO C2C***

***NO Sponsorship available***




About e360's App Engineering


e360 is a 30+ year privately-owned company with a focus on our people, our clients and leading technologies. e360's Cloud Services Division is a rapidly growing business helping clients manage their Cloud technology. Our team is comprised of leaders that focus on delivering innovative consulting solutions that leverage leading and emerging technologies.  

We are a dynamic and entrepreneurial consulting company that offers ample opportunities for professional development and growth suited to each individual's personal and professional goals. We offer internal, and subsidize external, trainings, and reimburse the cost of technology certification exams and / or renewals. Our family-founded business sees work life fit as a core value that all of our practitioners practice - the value you add to your team is more important than the time that you 'clock in and out.' You will have numerous opportunities to interface with senior leadership, and benefit from mentorship internally or through introductions through external networks to support your growth.  



Description



The Advanced Generative AI Developer is a hands-on consultant responsible for designing, building, and deploying production-ready Generative AI and agentic solutions on Google Cloud.

This role requires strong Python and cloud development experience, practical knowledge of Google Agent Development Kit, Gemini, Vertex AI, and GCP-native application and data services. The consultant will work directly with client and project teams to translate business requirements into secure, scalable, and maintainable AI solutions.



What You'll Do



  • Design, build, test, and deploy Generative AI applications and intelligent agents on Google Cloud.
  • Develop single-agent and multi-agent solutions using Google Agent Development Kit.
  • Integrate Gemini models with enterprise APIs, databases, applications, and business workflows.
  • Deploy AI applications using Agent Engine, Cloud Run, GKE, or other appropriate GCP services.
  • Build Retrieval-Augmented Generation solutions using services such as BigQuery, Vertex AI Vector Search, Cloud Storage, and Document AI.
  • Develop APIs, microservices, agent tools, MCP integrations, and event-driven workflows.
  • Build data pipelines to ingest, transform, chunk, embed, index, and retrieve structured and unstructured data.
  • Implement session management, memory, tool calling, human approval, and agent orchestration patterns.
  • Apply automated testing, CI/CD, logging, monitoring, tracing, evaluation, and cost-management practices.
  • Implement Google Cloud security using IAM, service accounts, Workload Identity Federation, Secret Manager, and private networking.
  • Troubleshoot issues across agents, models, APIs, data pipelines, integrations, security, and cloud deployments.
  • Create architecture diagrams, technical designs, API specifications, deployment guides, and operational documentation.
  • Own technical workstreams and provide design reviews, code reviews, and guidance to other developers.
  • Participate in client discovery, architecture, testing, deployment, and knowledge-transfer activities.

Requirements

  • Significant experience developing and deploying applications on Google Cloud.
  • Advanced Python development experience.
  • Hands-on experience building Generative AI or agentic applications.
  • Experience with Google Agent Development Kit, including agents, tools, workflows, sessions, state, and multi-agent patterns.
  • Experience integrating Gemini models using Vertex AI or Google Gen AI SDKs.
  • Experience with Agent Engine, Cloud Run, GKE, Cloud Functions, or similar GCP runtimes.
  • Experience designing and implementing RAG solutions.
  • Experience with BigQuery and Google Cloud data services.
  • Experience building APIs using frameworks such as FastAPI.
  • Experience with REST APIs, asynchronous processing, event-driven architecture, and microservices.
  • Understanding of MCP and its use in connecting agents to enterprise tools and systems.
  • Experience with SQL, document stores, object storage, embeddings, semantic search, or vector databases.
  • Experience with Git, automated testing, CI/CD, Docker, and infrastructure as code.
  • Understanding of Google Cloud IAM, service accounts, Secret Manager, networking, logging, and monitoring.
  • Ability to evaluate tradeoffs involving model quality, latency, security, scalability, reliability, and cost.

Candidates are not expected to have experience with every listed GCP service. However, they must have hands-on experience delivering Generative AI solutions and be able to explain their architecture and implementation decisions.




Preferred Qualifications



  • Experience delivering client-facing Google Cloud consulting projects.
  • Experience leading a technical workstream from discovery through production deployment.
  • Experience deploying ADK agents using Agent Engine, Cloud Run, or GKE.
  • Experience implementing MCP servers, custom agent tools, or enterprise integrations.
  • Experience with Vertex AI Vector Search, BigQuery Vector Search, Document AI, Apigee, Pub/Sub, Eventarc, or Workflows.
  • Experience with Terraform, Cloud Build, Artifact Registry, and automated GCP deployment pipelines.
  • Experience implementing AI evaluation, agent testing, observability, guardrails, and cost monitoring.
  • Relevant Google Cloud certifications.



Professional Skills



  • Strong consulting, communication, and problem-solving skills.
  • Ability to translate business requirements into practical technical solutions.
  • Ability to explain complex AI and cloud concepts to technical and non-technical stakeholders.
  • Strong documentation and technical leadership skills.
  • Ability to work independently and manage changing project priorities.
  • Ability to identify and communicate technical risks, dependencies, and blockers.
  • Willingness to mentor other developers and contribute to reusable delivery standards.



Critical Success Factors



  • Ability to independently design and deliver production-ready AI solutions on Google Cloud.
  • Strong practical knowledge of Google ADK, Gemini, Vertex AI, and GCP architecture.
  • Ability to build agents that securely interact with APIs, data, tools, and enterprise systems.
  • Ability to determine when to use agentic, deterministic, serverless, containerized, or managed-service patterns.
  • Commitment to security, testing, observability, governance, maintainability, and cost control.
  • Ability to own technical workstreams and consistently deliver high-quality client outcomes.

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