Automation QA Engineer

Comrise; Taguig; On-site; PHP 600,000 - 900,000; Full time

Job summary

Comrise is seeking an Automation QA Engineer to define and improve QA practices for AI-enabled solutions. You will automate evaluation, monitor production quality, and ensure compliance across accuracy, safety, and reliability.

Strong Python skills and Playwright experience are essential as you develop tests for UI, API, and workflows. You will collaborate with developers to refine prompts, tool definitions, and workflows while maintaining dashboards and runbooks for incident response and

Qualifications

• Minimum 3 years' experience in QA, test automation, or DevOps roles (or 2 years with AI/ML testing exposure).
• Strong Python skills for test automation, evaluation harnesses, and basic data analysis.
• High attention to detail with focus on reliability and user trust.
• Comfort with evolving tools, frameworks, and testing practices.
• Collaborative mindset, using evidence-based insights to influence decisions.

Responsibilities

• Define quality criteria and testing strategies for agent workflows, covering accuracy, latency, safety, compliance, and risk.
• Build automated evaluation harnesses to assess agent performance and potential issues.
• Implement continuous production monitoring to detect anomalies and safety concerns.
• Develop and maintain automated test suites using Playwright for UI and API/workflow validation.
• Apply LLM evaluation frameworks to assess output quality, regression, and drift over time.
• Produce dashboards and reports communicating quality metrics to engineering and stakeholders.
• Develop runbooks and contribute to incident response; collaborate to improve prompts and tool definitions.
• Ensure testing, logging, and monitoring align with data privacy and regulatory requirements.

Skills

Python

Test automation

Data analysis

Attention to detail

Collaborative mindset

Tools

Playwright

Deepeval

RAGAS

Evidently.AI

APIs

Prometheus

Grafana

SQL

Pytest

Job 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.

Key Accountabilities

• 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

Knowledge, Skills & Experience

• 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

Technical Skills (Required)

• 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
• Pytest or equivalent testing frameworks
• SQL for querying logs, metrics, or evaluation datasets
• Prometheus, Grafana, or similar monitoring tools
• Familiarity with hallucination detection and AI safety patterns