Software Automation Engineer

McKesson•Usa, VA

2d•Remote

About The Position

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you. 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 Software Automation Engineer (P3) 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 Lead, 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 ML Ops teams to validate both traditional software workflows and AI driven behaviors, including prompt based systems, retrieval augmented generation (RAG), and model integrated services.

Requirements

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.
Solid understanding of QA methodologies, test design techniques, and quality metrics.
Ability to analyze performance issues related to CPU, memory/heap, garbage collection, threads, databases, messaging systems, and network latency.
Strong understanding of limitations and risks of AI generated outputs , with the ability to validate , correct, and harden results for production quality use.
Ability to manage multiple priorities and work independently in a fast paced environment.

Nice To Haves

Experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI and build tools like Maven or Gradle.
Hands-on experience with performance testing tools (JMeter, NeoLoad , or similar).
Experience using profiling and monitoring tools (Dynatrace, New Relic, AppDynamics, Splunk, JProfiler ).
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
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.