Senior QA Engineer

We are looking for a Senior QA Engineer to own quality assurance for a next-generation Intelligent Document Processing (IDP) platform a system that combines OCR AI/LLM-based extraction and modern backend/cloud architecture to automatically process and classify documents at scale.

This is a role for someone who can think beyond test cases and actually architect a QA strategy from the ground up someone whos tested complex multi-layered systems before (web cloud database and AI-driven components) and knows how to bring order rigor and best practices to a system where not every output is simply right or wrong. Youll be defining what quality even means for an AI-powered pipeline not just executing a checklist.

Test Strategy & Leadership

• Define and own the overall QA strategy and test vision for the platform from unit level up through full end-to-end system validation.
• Introduce and champion QA best practices testing standards and a quality-first culture across the engineering team.
• Build a testing roadmap that scales with the product balancing manual automated and AI-specific evaluation approaches.
• Act as the quality gatekeeper for releases with clear go/no-go criteria backed by data.

Testing Coverage

• Unit Testing Partner with developers to ensure adequate unit test coverage and quality at the code level.
• Integration Testing Validate interactions between services APIs databases and third-party components (OCR engines AI/LLM services storage queues).
• End-to-End Testing Design and execute E2E test scenarios that simulate real document-processing workflows from ingestion to final output.
• Regression Testing Build and maintain a reliable regression suite to catch breakages introduced by code model or prompt changes.
• Smoke Testing Establish fast lightweight smoke test suites for quick health checks after every deployment.
• Database Testing Validate data integrity schema correctness migrations and data transformations across the pipeline; write and optimize SQL queries to verify stored/processed data.
• Automation Testing Design and build scalable automation frameworks; identify what to automate vs. test manually for maximum ROI.
• AI/Model Output Evaluation Design evaluation approaches for probabilistic outputs (OCR/AI extraction) including accuracy benchmarking consistency checks and handling of non-deterministic results going beyond simple pass/fail assertions.

Quality Ownership

• Define meaningful quality metrics (accuracy precision/recall defect leakage test coverage etc.) and report on them clearly to stakeholders.
• Own test planning test case design bug tracking/triage and release sign-off.
• Build and maintain a reusable test data corpus (documents expected outputs edge cases) to support ongoing regression and evaluation.
• Collaborate closely with engineering and product teams to catch issues early shifting quality left in the development lifecycle.
• Mentor and guide other QA resources as the team grows setting the tone for testing rigor and craftsmanship.

Must-Haves:

• 6 years of QA experience with a strong track record testing complex web and cloud-based applications end-to-end.
• Proven experience owning full-spectrum testing unit integration system/E2E regression and smoke testing not just one layer.
• Strong hands-on database testing skills comfortable writing SQL queries validating data integrity and testing data pipelines/transformations.
• Solid experience with test automation frameworks and building automation strategy from scratch.
• Experience testing AI ML or NLP-powered systems or systems with non-deterministic/probabilistic outputs understands that traditional pass/fail testing isnt enough for these.
• Demonstrated ability to define test strategy and vision not just execute existing test plans has built or matured a QA function/process before.
• Strong experience with API testing and modern CI/CD-integrated testing pipelines.
• Excellent analytical and root-cause/bug triage skills across multi-layered systems.
• Strong communication skills able to represent quality status and risk clearly to technical and non-technical stakeholders.

Nice-to-Haves:

• Experience testing OCR or document-processing systems.
• Experience testing LLM/GenAI-integrated applications or prompt-based pipelines.
• Scripting ability (any language) to build custom test tools/harnesses.
• Experience with performance/load testing for high-volume batch-processing systems.
• Background in regulated or document-heavy industries.

• A mature well-structured test strategy covering every layer of the system from unit to end-to-end.
• A regression and smoke suite the team trusts to catch issues before every release.
• Clear data-backed quality metrics that give leadership confidence in whats shipping.
• A QA culture and set of best practices that elevate how the whole engineering team thinks about quality not just a person running test cases.