AI Automation QA

Description

The Automation QA Engineer defines, implements, and continuously improves quality assurance practices for AI enabled and agent based solutions. The role ensures systems meet agreed standards for accuracy, reliability, performance, safety, and compliance, and supports operational readiness through automated evaluation and monitoring.

The role:

Definition and execution of testing and quality assurance strategies for AI enabled workflows

Continuous evaluation and monitoring of system behavior in production environments

Contribution to auditability, risk management, and continuous quality improvement

Define quality criteria and testing strategies for agent workflows, covering accuracy, latency, safety, compliance, and operational risk

Build automated evaluation harnesses to assess agent performance, including hallucination rates, tool misuse, policy violations, and task success

Implement continuous production monitoring to detect anomalies, quality degradation, and emerging safety concerns

Develop and maintain automated test suites using Playwright for UI testing and custom scripts for API and workflow validation

Apply LLM evaluation frameworks to assess output quality, regression, and system drift over time

Produce and maintain dashboards and reports that communicate quality metrics and trends to engineering and stakeholders

Develop and maintain runbooks for common failure modes and contribute to incident response activities

Collaborate closely with developers to improve prompts, tool definitions, and workflow designs based on test results

Ensure testing, logging, and monitoring practices align with data privacy, audit, and regulatory requirements

Qualifications

Minimum 3 years’ experience in QA, test automation, or DevOps roles (or 2 years with direct experience testing AI or ML enabled systems)

Strong Python skills for test automation, evaluation harnesses, and basic data analysis

High attention to detail, with a focus on issues that materially impact reliability and user trust

Comfort working with evolving tools, frameworks, and testing practices

Collaborative mindset, using evidence based insights to influence product and engineering decisions

Programming: Python (test automation, evaluation harnesses, data analysis)

UI Automation: Playwright (end‑to‑end workflow testing)

AI Evaluation: Deepeval, RAGAS, Evidently.AI (LLM quality, drift, and regression analysis)

Workflow Testing: API and agent workflow validation using custom scripts

Monitoring: Production quality monitoring and anomaly detection