Senior Quality Assurance Engineer - IDP Platform

About the role

A next-generation Intelligent Document Processing (IDP) platform combining OCR, AI/LLM-based extraction and modern backend/cloud architecture is being built. The company is looking for a Senior QA Engineer to own quality assurance from the ground up, defining what quality means for an AI-powered pipeline rather than just checking off test cases. The role suits someone who has tested complex, multi-layered systems (web, cloud, database and AI components) and can bring rigor and best practices to a system where outputs are not always simply right or wrong.

Key responsibilities

• Own the overall QA strategy and test vision, from unit tests to full end-to-end system validation, and build a testing roadmap that balances manual, automated and AI-specific evaluation.
• Act as quality gatekeeper for releases, with clear go/no-go criteria backed by data.
• Partner with developers on unit test coverage, validate integrations between services, APIs, databases and third-party components, and run E2E scenarios covering real document-processing workflows.
• Maintain regression and smoke suites, and build scalable automation frameworks while deciding what to automate versus test manually.
• Evaluate AI and model outputs (OCR/AI extraction) for accuracy, consistency and robustness against non-deterministic results.
• Define quality metrics such as accuracy, precision/recall, defect leakage and test coverage, and report them to stakeholders.
• Own test planning, test case design, bug tracking and triage, and build a reusable test data corpus.
• Shift quality left with engineering and product teams, mentor other QA resources and promote a quality-first culture.

Required profile

• 6+ years of QA experience testing complex web and cloud applications end-to-end.
• Proven ownership of full-spectrum testing: unit, integration, system/E2E, regression and smoke.
• Strong database testing skills, including writing SQL queries and validating data pipelines and transformations.
• Experience testing AI, ML or NLP systems and understanding that traditional pass/fail testing is insufficient for probabilistic outputs.
• Demonstrated ability to define test strategy and vision, having built or matured a QA function before.
• Excellent analytical, root-cause and bug-triage skills, and strong communication with technical and non-technical stakeholders.

Nice-to-haves

• Testing of OCR or document-processing systems, or LLM/GenAI and prompt-based pipelines.
• Scripting ability to build custom test tools.
• Performance/load testing for high-volume batch processing.
• Background in regulated or document-heavy industries.