AI QA Automation Engineer

Job Overview

The AI QA Automation Engineer will ensure the quality, reliability, and scalability of AI models, pipelines, and services. This remote role emphasizes building and maintaining an automated test infrastructure for rapid model delivery, proactive quality assurance, and ongoing production monitoring within a DevOps-driven environment.

Key Responsibilities

• Conduct comprehensive testing across all product layers: server load, integrations, and model output quality.
• Apply Test Driven Development (TDD): define necessary tests before feature development.
• Proactively identify testing requirements and communicate them before build phases.
• Design, develop, and maintain automated test suites (unit, integration, performance, end-to-end).
• Maximize automation coverage, reliability, and repeatability; minimize manual testing.
• Collaborate with DevOps to tightly integrate tests into CI/CD pipelines for models/data pipelines.
• Implement and maintain robust API contract testing across interconnected services.
• Execute and refine manual tests (especially for LLM outputs: hallucinations/factuality/edge cases).
• Configure and interpret dashboards/alerts with tools like Prometheus, Grafana, CloudWatch.
• Track post-deployment model and pipeline health, triage production quality issues.
• Advocate for automation, anticipate potential issues, and champion testing best practices company-wide.

Must-Have Skills and Competencies

• Proven expertise in test automation, performance, and reliability engineering for AI or ML systems.
• Hands-on practice with automated QA tools: Pytest, Playwright, Postman, Langfuse (or similar).
• Deep experience integrating automated testing with CI/CD (DevOps) workflows for ML models and data pipelines.
• Sound understanding of manual exploratory testing—especially for complex, evolving LLM-based outputs.
• Strong experience with monitoring/observability: Prometheus, Grafana, CloudWatch, or similar.
• Excellent problem-solving, ownership, and communication skills.
• Proactive mindset with a relentless focus on automation and quality.
• Collaborative spirit—works seamlessly with product, engineering, and DevOps teams.

Nice to Have

• Previous experience testing and monitoring Large Language Models (LLMs) or generative AI systems.
• Knowledge of Langfuse or purpose-built LLM testing/tracking frameworks.
• Familiarity with other performance/monitoring tools or APM systems.
• Background in data engineering or ML Ops environments.

How to Prepare for the Interview

• Demonstrate test automation skills using Pytest, Playwright, and Postman; show examples of real test cases for APIs and web apps.
• Prepare to explain in detail how you’ve built or extended CI/CD pipelines to include automated tests for ML/AI models.
• Review strategies for testing/monitoring LLMs, including handling hallucinations and edge cases.
• Gain hands-on experience with observability/monitoring tools (choose either Prometheus + Grafana or AWS CloudWatch).
• Practice explaining TDD and how you would “think ahead” on testing needs.
• Be ready to showcase problem-solving methodology and proactive actions you've implemented in a Quality role.
• Prepare examples of cross-team collaboration, requirement communication, and test advocacy.

How to Prepare the Resume

• Highlight roles/projects where you established or extended automated testing for AI/ML solutions.
• List all relevant tools and frameworks (Pytest, Playwright, Postman, Langfuse, Prometheus, Grafana, CloudWatch, etc.).
• Quantify improvements: e.g., increased test coverage, reduced manual effort, or speedier production releases through automation.
• Include examples testing APIs, web integrations, and model output quality.
• Showcase CI/CD pipeline integration and post-deployment monitoring/alerting work.
• Emphasize a quality-first, automation-driven mindset and your ability to work collaboratively across teams.

Useful Links for Preparation

• Pytest for Python Automation: https://docs.pytest.org/en/7.4.x/
• Playwright Testing Guide: https://playwright.dev/docs/intro
• Postman API Automation Basics: https://www.guru99.com/postman-tutorial.html
• Langfuse (LLM Output Testing): https://langfuse.com/docs
• TDD Overview and Practices: https://martinfowler.com/bliki/TestDrivenDevelopment.html
• Prometheus Monitoring: https://prometheus.io/docs/introduction/overview/
• Grafana Dashboards: https://grafana.com/docs/grafana/latest/
• AWS CloudWatch for Monitoring: https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/WhatIsCloudWatch.html
• Testing LLMs: https://www.testdevlab.com/blog/testing-large-language-models-best-practices