Software QA Automation Engineer

McKesson Compile; Bengaluru; On-site; INR 1,000,000 - 1,500,000; Full time

Job summary

McKesson Compile is seeking a Lead QA Automation Engineer in Bengaluru, India. This senior role focuses on designing and maintaining automated test solutions across Ontada's product ecosystem, emphasizing AI-enabled systems.

The ideal candidate will bring extensive experience in software quality assurance and automation, ensuring product quality through comprehensive test strategies and advanced testing methodologies.

The position requires collaboration across teams and expertise in handling AI/ML features with an emphasis on compliance and safety standards.

Qualifications

• Degree or equivalent with typically 6+ years of relevant experience.
• 7+ years of progressive Software Quality Assurance experience.
• 5+ years of hands-on test automation development experience.
• Strong experience working in Agile SDLC environments with CI/CD pipelines.

Responsibilities

• Design, develop, and maintain automated test solutions across products.
• Own and execute test strategy, planning, and execution for features.
• Perform exploratory testing to uncover complex defects.
• Integrate automated tests into CI/CD pipelines.

Skills

Software Quality Assurance

Test Automation Development

API Testing

Backend Testing

AI Testing

Agile SDLC

Proficiency in Java

Performance Testing

Education

Bachelor's degree in Computer Science or equivalent

Tools

Selenium

RestAssured

Jenkins

JMeter

Job description

About McKesson

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.

Company Location

Based in Bangalore, India, McKesson’s data is a comprehensive, fully linked system of record for the US Healthcare market.

About Ontada

Ontada is a leading oncology real‑world data and evidence, clinical education, and provider technology business dedicated to transforming the fight against cancer. It is part of McKesson Corporation.

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.

Key Responsibilities

• 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.
• 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 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.
• 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, and 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.
• 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 such as message queues, APIs, and batch processes.
• 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.
• 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, and 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+ year of experience testing AI/ML or GenAI systems or demonstrated delivery of AI‑adjacent quality frameworks.
• 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.
• 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.