Lead QA Automation Engineer
Job Description
About McKesson Compile
Established in 1833, McKesson is a US Fortune 10 global leader in healthcare supply chain management solutions, retail pharmacy, healthcare technology, community oncology, and specialty care. We partner with life sciences companies, manufacturers, providers, pharmacies, governments, and other healthcare organizations to help provide the right medicines, medical products, and healthcare services to the right patients at the right time, safely and cost effectively.
Based in Bangalore India, McKesson Compile’s data is a comprehensive, full linked system of record for the US Healthcare market, with intelligence on 2M+ healthcare professionals (HCPs) and over 800K facilities. Compile’s data includes high capture medical and pharmacy claims, closed capture Medicare claims (100%), along with best-in-class provider affiliations and customer master.
At McKesson we deliver careers with purpose and potential. Our focus on better health starts with creating an inclusive environment with strong values where you can build a fulfilling career. You can count on us to provide you with resources and opportunities to grow and be your best, while contributing to our pursuit of improving lives.
Ontada is a leading oncology real‑world data and evidence, clinical education, and provider technology business dedicated to transforming the fight against cancer. Part of McKesson Corporation, we support science through our data, technology, and channels, accelerating innovation for life science companies, empowering community oncology providers, and advancing patient care. Together with our partners, we improve the lives of cancer patients.
Position Summary
The Lead QA Automation Engineer is a senior individual contributor responsible for designing, developing, and maintaining automated test solutions across Ontada’s product ecosystem, with a strong focus on AI-enabled and GenAI-powered systems. Reporting to the QA Manager, this role drives high-quality automation for UI, API, backend, and data layers, while ensuring AI/ML features meet expectations for correctness, reliability, safety, and compliance.
This role partners closely with Product, Engineering, and Operation teams to validate both traditional software workflows and AI-driven behaviors, including prompt-based systems, retrieval-augmented generation (RAG), and model-integrated services
Key Responsibilities
Quality Engineering Ownership & Execution
Own and execute test strategy, planning, and execution for assigned features, services, or product areas
Identify functional, integration, and non‑functional quality risks early; communicate risks, impacts, and recommendations clearly.
Author comprehensive test strategies, test plans, and test cases aligned with product requirements and acceptance criteria.
Perform exploratory testing to uncover complex, edge‑case, and systemic defects.
Coordinate end‑to‑end validation across multiple environments to ensure release readiness.
Test Automation & Framework Development
Design, develop, and maintain automated test suites across UI, API, service, and data layers.
Contribute to the enhancement and maintainability of automation frameworks using tools such as Selenium, TOSCA, etc.
Develop robust API automation using RestAssured, Postman, or equivalent frameworks.
Implement effective test data strategies, including synthetic data generation and environment setup.
Integrate automated tests into CI/CD pipelines to support fast and reliable feedback cycles.
Leverage AI‑assisted development tools (e.g., GitHub Copilot, Claude Code, or similar) to accelerate test automation development, refactoring, and debugging while maintaining code quality and security standards.
Use AI tooling to assist with test case generation, edge‑case identification, and data‑driven scenario expansion, validating all outputs through engineering judgment and established QA practices.
AI / GenAI Quality Engineering
Design and execute test strategies for AI/ML and GenAI‑powered features, including LLM‑based workflows.
Validate prompt behavior, prompt templates, and prompt chaining across different scenarios and data contexts.
Perform negative testing for AI systems, including prompt injection, jailbreak attempts, hallucination risks, and unsafe outputs.
Test Retrieval‑Augmented Generation (RAG) pipelines, including:
Embedding quality validation
Retrieval accuracy, recall, and relevance
Chunking and indexing strategies
Validate AI outputs for accuracy, consistency, explainability, and compliance in regulated environments.
Collaborate with Engineering and Operations teams to test model integrations, configuration changes, and inference pipelines.
Utilize AI‑powered tools to support prompt analysis, test scenario exploration, and hypothesis generation when validating LLM‑based features and AI workflows.
Critically evaluate AI‑generated suggestions and outputs to ensure accuracy, safety, reproducibility, and regulatory compliance.
Backend & Data Testing
Perform advanced backend testing across SQL and NoSQL data systems.
Validate data ingestion, transformations, persistence, and integrity across services and environments.
Coordinate testing of asynchronous workflows and integrations (e.g., message queues, APIs, batch processes).
Agile Collaboration
Work closely with Product Owners and Business Analysts to refine user stories, define acceptance criteria, and ensure testability.
Partner with developers during design and implementation to support shift‑left testing.
Participate actively in sprint planning, grooming, retrospectives, and release readiness reviews.
Collaborate with onshore and offshore QA team members to ensure consistent execution and quality standards.
Quality Governance, Compliance & Continuous Improvement
Ensure testing activities align with HIPAA and other regulatory, security, and data‑privacy requirements.
Contribute audit‑ready documentation, including test plans, execution evidence, and reports.
Participate in root‑cause analysis for quality or performance issues and support corrective actions.
Identify opportunities for improving QA processes, tools, and documentation; contribute suggestions through established continuous improvement channels.
Research and evaluate new QA, automation, or performance testing, AI assisted tools and techniques as appropriate.
Minimum Requirement
Degree or equivalent and typically requires 6+ years of relevant experience.
Education
Bachelor’s degree in computer science, Engineering, Mathematics, or equivalent practical experience.
Critical Skills
7+ years of progressive Software Quality Assurance experience, preferably in healthcare or regulated industries.
5+ years of hands‑on test automation development experience.
5+ years of API testing and automation experience.
5+ years of backend testing experience using SQL and/or NoSQL databases.
1+ years of experience testing AI/ML or GenAI systems or demonstrated delivery of AI‑adjacent quality frameworks (e.g., prompt testing, RAG evaluation, guardrails).
Experience owning QA execution for complex product areas with limited day‑to‑day oversight.
Experience mentoring or supporting junior QA engineers.
Strong experience working in Agile SDLC environments with CI/CD pipelines.
Proficiency in Java, JavaScript, or Python for test automation and scripting.
Experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI and build tools like Maven or Gradle.
Solid understanding of QA methodologies, test design techniques, and quality metrics.
Hands‑on experience with performance testing tools (JMeter, NeoLoad, or similar).
Experience using profiling and monitoring tools (Dynatrace, New Relic, AppDynamics, Splunk, JProfiler).
Ability to analyze performance issues related to CPU, memory/heap, garbage collection, threads, databases, messaging systems, and network latency.
Experience creating reusable, maintainable, and portable automation and performance test scripts.
RAG testing experience, including embedding quality, retrieval evaluation, and chunk strategy validation.
Familiarity with vector databases and semantic search concepts.
Hands‑on experience using AI‑assisted coding and analysis tools such as GitHub Copilot, Claude Code, or similar.
Ability to apply AI tools effectively for:
Test automation development and refactoring
Debugging and root‑cause investigation
Exploratory test design and edge‑case discovery
Strong understanding of limitations and risks of AI‑generated outputs, with the ability to validate, correct, and harden results for production‑quality use.
Additional Skills
Experience with source control tools such as GitHub, Bitbucket, Git Bash.
Experience with test management tools (qTest, TestRail, ALM, TestLink, or similar).
Familiarity with microservices and distributed system architectures.
Experience benchmarking, capacity planning, and release readiness reporting.
Knowledge of healthcare software, data privacy, and regulatory compliance is a plus.
Ability to manage multiple priorities and work independently in a fast‑paced environment.
Working Conditions
Full-time in office role based in Bangalore, India (IST).
Collaboration with distributed, cross‑functional teams.
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