About the role
About the Role
We are looking for a skilled QA Engineer to join a data and AI-driven engagement in the pharma commercial space. You will be responsible for ensuring the quality, reliability, and AI-readiness of a modern application stack spanning React frontends and Python-based backend services — including AI/ML components.
Key Responsibilities
Design, develop, and execute test plans covering frontend (React) and backend (Python/REST APIs) components
Build and maintain automated test suites for UI, API, and integration layers
Conduct AI readiness assessments — validating model inputs/outputs, data pipelines, edge cases, hallucination risks, and performance thresholds for AI-powered features
Collaborate with data scientists and engineers to define acceptance criteria for ML models and AI algorithms
Perform regression, functional, performance, and UAT testing across release cycles
Identify, document, and track defects; work with dev teams to resolve and retest
Contribute to CI/CD pipelines by integrating automated tests and quality gates
Validate data accuracy and consistency relevant to pharma commercial workflows (e.g., sales force, HCP/HCO data, market access)
Participate in sprint ceremonies and provide QA sign-off for production releases
Required Skills & Experience
4–6 years of hands-on QA/testing experience in software product or analytics environments
Strong experience testing React-based frontend applications (component testing, E2E using tools like Cypress, Playwright, or Selenium)
Proficiency in backend/API testing with Python (pytest, requests, or similar frameworks)
Exposure to testing AI/ML systems — model validation, output consistency checks, data drift, and non-deterministic behavior handling
Familiarity with CI/CD tools (GitHub Actions, Jenkins, or similar) and integrating tests into pipelines
Solid understanding of test design principles — boundary value, equivalence partitioning, exploratory testing
Strong documentation skills for test cases, defect reports, and QA sign-off summaries
Good to Have
Prior experience in the pharma commercial domain — understanding of HCP/HCO targeting, sales force effectiveness, market access, or promotional analytics
Experience with performance/load testing tools (Locust, JMeter, k6)
Familiarity with MLflow, model registries, or AI observability tools
Understanding of data quality frameworks and validation for structured/unstructured data
Soft Skills
Strong analytical and problem-solving mindset
Ability to work in cross-functional teams with engineers, data scientists, and business stakeholders
Clear communication — able to explain defects and quality risks to both technical and non-technical audiences
Self-driven with attention to detail in ambiguous, fast-paced environments
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