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 who's 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. You'll be defining what "quality" even means for an AI-powered pipeline, not just executing a checklist. ### What You'll Do 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. ### What We're Looking For 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 isn't 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. ### What Success Looks Like - 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 what's 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.