Senior QA Engineer

Integriti Group Inc.; Lahore; On-site; PKR 1,800,000 - 3,200,000; Full time

Job summary

Integriti Group Inc. is seeking a Senior QA Engineer to own quality assurance for a next-generation IDP platform. You will architect QA strategy for AI-driven extraction, OCR, and cloud/backend components, beyond simple test cases.

Lead testing coverage across unit, integration, E2E, and regression, while defining success metrics and risk governance. You will shape QA practices, build scalable automation, and mentor teammates, ensuring a quality-first culture and robust release readiness across

Qualifications

• Requires 6+ years of QA experience across web and cloud platforms.
• Ownership of full-spectrum testing from unit to E2E and regression.
• Strong SQL/database testing, data integrity, and data pipelines validation.
• Proven automation strategy development and CI/CD integration.
• Experience testing AI/ML/NLP systems and non-deterministic outputs.
• Ability to define test strategy and drive QA maturity across teams.

Responsibilities

• Define and own the QA strategy and test vision for the platform.
• Champion QA standards and a quality-first culture across engineering.
• Create a scalable testing roadmap balancing manual, automated, and AI evaluation.
• Act as the quality gatekeeper for releases with data-backed go/no-go criteria.
• Design and execute unit, integration, E2E, regression, and smoke tests.
• Validate data integrity across pipelines and perform SQL validation.
• Develop automation frameworks and determine what to automate for ROI.
• Evaluate AI/model outputs, benchmarking accuracy and consistency.

Skills

QA strategy

End-to-end testing

Database testing

Test automation

API testing

CI/CD testing

Communication

Bug triage

Tools

Automation frameworks

Job description

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.