# Software Development Engineer In Test III - AI

**Company:** [GoHighLevel](https://hotfix.jobs/companies/gohighlevel)
**Location:** Remote
**Role:** QA Engineering
**Experience:** 5+ years
**Skills:** OpenAI, Claude, Ai/Ml Testing, RAG, Playwright, Cypress, Selenium, Postman, Rest, GraphQL, gRPC, CI/CD, AWS, GCP, MLOps
**Posted:** 2024-05-07

> Designs and maintains automated test strategies for AI-powered products, validating LLM integrations and RAG pipelines while improving coverage and release quality. The role requires 5+ years in QA/SDET, strong automation and API testing skills, independent ownership, and mentorship capabilities.

## Job Description

## Responsibilities
- Design, develop, and maintain automation frameworks and test strategies for AI products.
- Test and validate AI-powered features, including OpenAI, Claude, and other AI integrations.
- Evaluate and improve Retrieval-Augmented Generation (RAG) flows for accuracy and performance.
- Leverage AI automation tools to optimize test coverage and efficiency.
- Collaborate with developers, product managers, and AI researchers to ensure high-quality releases.
- Own tasks end-to-end and work independently to deliver on commitments.
- Mentor and guide junior SDETs, fostering a culture of quality and continuous learning.

## Requirements
- 5+ years of overall experience in QA/SDET roles.
- Proven experience working with AI tools such as OpenAI, Claude, or similar large language model platforms.
- Strong understanding of testing AI/ML products and their unique challenges.
- Hands-on knowledge of RAG pipelines and testing methodologies.
- Proficiency in automation frameworks such as Playwright, Cypress, Selenium, or similar.
- Experience with API testing using Postman, REST, GraphQL, or gRPC.
- Strong problem-solving skills and the ability to work independently.
- Mentorship and leadership skills for guiding peers or junior engineers.

## Nice to Have
- Experience with CI/CD pipelines and cloud platforms such as AWS, Google Cloud, or Azure.
- Familiarity with MLOps pipelines and monitoring AI systems.
- Exposure to performance and scalability testing of AI workloads.

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