McKesson · 8 hours ago
Software Automation Engineer
United States
Full-time
Remote
Mid, Senior Level
$105K/yr - $175K/yr
4+ years expMcKesson is an impact-driven, Fortune 10 company that focuses on delivering insights and services to improve healthcare accessibility. The Software Automation Engineer will design, develop, and maintain automated test solutions, particularly for AI-enabled systems, ensuring high-quality automation across various product layers while collaborating with cross-functional teams.
BiopharmaBiotechnologyHealth CareInformation TechnologyPharmaceutical
No H1B
Responsibilities
Own and execute test strategy, planning, and execution for assigned features, services, or product areas under the guidance of the QA Lead
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, Playwright, Cypress, TOSCA, or similar
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
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 MLOps 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 (e.g., message queues, APIs, 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; contribute suggestions through established continuous improvement channels
Research and evaluate new QA, automation, or performance testing, AI assisted tools and techniques as appropriate
Qualification
Required
Degree or equivalent and typically requires 4+ years of relevant experience
Bachelor's degree in computer science, Engineering, Mathematics, or equivalent practical experience
4+ years of progressive Software Quality Assurance experience, preferably in healthcare or regulated industries
3+ years of hands‑on test automation development experience
2+ years of API testing and automation experience
3+ years of backend testing experience using SQL and/or NoSQL databases
3+ years of software performance testing experience, including test planning, execution, and analysis
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
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
Benefits
Competitive compensation package
Annual bonus
Long-term incentive opportunities
Company
McKesson
McKesson distributes medical supplies, information technology, and care management products and services.
10001+ employees
Funding
Current Stage
Public CompanyTotal Funding
unknown1994-11-18IPO
Leadership Team
Brian Tyler
CEO
Jim McAuliffe
CFO
Recent News
2026-01-23
2026-01-12
Company data provided by crunchbase