# Lead Software Development Engineer in Test - AI

**Company:** [GoHighLevel](https://hotfix.jobs/companies/gohighlevel)
**Location:** Remote
**Role:** QA Engineering
**Experience:** 7+ years
**Skills:** Ai Testing, Model Testing, RAG, Hallucination Testing, Drift Testing, Bias Testing, Test Automation, CI/CD, AWS, GCP, Azure, MLOps, Ai Monitoring, Performance Testing, Scalability Testing
**Posted:** 2025-11-13

> Leads quality strategy for the AI vertical, including scalable automation frameworks and testing for RAG, hallucination, drift, and bias. The role requires 7+ years in QA/SDET work, strong technical leadership, and experience with AI testing methodologies.

## Job Description

## Responsibilities
- Define the overall QA strategy and architect large-scale, reusable, cross-platform automation frameworks.
- Drive organization-wide quality initiatives and own the quality charter for the AI vertical.
- Design scalable testing approaches for RAG, hallucination, drift, and bias in AI tools and models.
- Own decisions on test strategy, tools, frameworks, and AI testing methodologies.
- Coach and upskill the entire team, establish best practices, and build a talent pipeline.
- Influence cross-functional teams, participate in architecture discussions, and align testing with business strategy.
- Own end-to-end quality strategy at the product or vertical level.
- Shape the AI quality roadmap, reduce organization-wide risk, and improve scalability and reliability.

## Requirements
- 7+ years of overall experience in QA or SDET roles.
- Deep expertise in AI tools and model testing.
- Experience designing scalable test approaches for RAG, hallucination, drift, and bias.
- Proven experience architecting large-scale, reusable, cross-platform automation frameworks.
- Strong leadership skills, including coaching teams, setting best practices, and building talent pipelines.
- Ability to influence cross-functional teams and participate in architecture discussions.
- Strong problem-solving skills and the ability to make decisions about test strategy, tools, frameworks, and AI testing methodologies.

## Nice-to-Haves
- 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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