QA Engineer

About Transform:

Transform is an AI-native platform built to help companies and consulting teams accelerate digital transformation with embedded AI and agents. Our platform connects communication tools, project artifacts, enterprise systems, and delivery workflows into a unified system of record that supports the full transformation lifecycle, from discovery and design through development, testing, deployment, and ongoing support.

We work across industries to help organizations reduce delivery friction, improve visibility, preserve institutional knowledge, and execute complex initiatives with greater confidence.

At Transform, we are building the future of AI-enabled delivery and are looking for team members who thrive in fast-moving environments, enjoy solving complex problems, and want to help shape the next generation of enterprise software.

About the Role:

We are looking for a senior, hands-on QA Engineer who takes full ownership of quality across the entire delivery cycle. At Transform, AI is not a feature, it is the foundation of everything we build. You will use AI tools daily, test AI-driven workflows extensively, and be expected to bring a deep understanding of how large language models and agents behave in order to design meaningful, edge-case-aware test strategies.

You will work closely with product managers, engineers, and stakeholders to define what done looks like, catch issues before they reach customers, and continuously raise the bar on how we test and ship. You bring fluency in English, a sharp eye for edge cases, a bias toward action, and the technical curiosity to automate the repetitive so you can focus on what matters most.

Responsibilities:

• Design, execute, and maintain automated test cases across multiple environments (development, staging, production), ensuring thorough coverage of functional, regression, and AI-specific scenarios.
• Test AI-powered features and agent workflows with a deep understanding of LLM behavior, including prompt sensitivity, non-determinism, hallucination patterns, and edge case handling.
• Own and refine acceptance criteria in collaboration with product managers and developers, ensuring tickets are testable, clearly scoped, and account for AI variability before work begins.
• Partner with stakeholders to improve ticket quality — surfacing ambiguities, edge cases, and missing context early in the cycle.
• Identify, document, and track defects with clear reproduction steps, severity assessments, and timely resolution follow-up.
• Build and maintain automation scripts for repetitive or high-frequency flows, leveraging AI tooling where appropriate to maximize efficiency.
• Triage and prioritize QA tasks proactively based on sprint goals, release timelines, and risk exposure.
• Participate in sprint ceremonies — planning, refinement, and retrospectives — contributing a quality-first perspective throughout the delivery cycle.

Qualifications:

Essential Qualification:

• 5+ years of hands-on experience in QA testing for web-based or SaaS applications, with a strong focus on manual testing and environment management.
• Deep, working knowledge of AI, including large language models, prompt engineering, agent architectures, and the behavioral patterns that make AI systems difficult to test reliably.
• Fluent English (written and spoken), required for effective collaboration with international stakeholders, writing precise bug reports, and contributing clearly in async and real-time communication.
• Proven ability to define, manage, and validate acceptance criteria across complex, multi-stakeholder features involving AI-driven outputs.
• Strong defect reporting skills, clear, reproducible, and appropriately prioritized, including for non-deterministic AI behaviors.
• Proficiency with test and project management tools such as Jira, Linear, or TestRail.
• Foundational knowledge of test automation concepts and at least one framework (e.g., Playwright, Cypress, or Selenium).
• Excellent cross-functional communicator with the confidence to influence quality standards across product, engineering, and delivery teams.

Desired Qualification:

• Hands-on experience building or maintaining automated test suites integrated into a CI/CD pipeline.
• Experience evaluating and testing LLM outputs at scale, including adversarial prompting, output consistency checks, and AI regression strategies.
• Familiarity with AI development frameworks or tooling (e.g., LangChain, Mastra, OpenAI API) from a QA or integration-testing perspective.
• Experience with API testing tools such as Postman or Insomnia.
• Background in fast-paced agile teams with short release cycles and continuously evolving AI-driven features.