McKesson
Healthcare
Sr.MachineLearningOpsEngineer
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“Sr. Machine Learning Ops Engineer at McKesson. Skills: Machine Learning Ops, Generative AI, Cloud Platforms, CI/CD. Lead deployment of ML models and GenAI solutions. Automate high-impact model use cases”
What You'll Achieve.
Operationalizing machine learning and Generative AI solutions at scale; Consistent, reliable, and optimized delivery of data science innovations; Meet enterprise standards
Industry & Context.
2x per week onsite in Mississauga, ON
What They're Looking For.
Must Have
Experience deploying ML models into production environments, Hands-on expertise with CI/CD pipelines, monitoring, and production ML systems, Experience with GenAI or agentic AI frameworks, Knowledge of model observability, drift detection, and operational support, Experience working in scaling or early-stage ML environments, Proficiency with cloud platforms (AWS, Azure, or GCP), cross-functional collaboration skills (Data Science, Product, Architecture), Ability to drive standardization, automation, and platform maturity, Focus on reliability, scalability, and optimization, Degree or equivalent and typically requires 7+ years of relevant experience
Nice to Have
Experience with Databricks ecosystem (e.g., Databricks Genie), Familiarity with LangChain, LangGraph, or Microsoft Semantic Kernel, Exposure to GenAI cost optimization / FinOps practices, Experience implementing secure enterprise applications (e.g., Okta), Experience in healthcare or regulated environments, Experience scaling ML/AI capabilities from experimentation to production maturity
What You'll Do.
Lead deployment of ML models and GenAI solutions
Automate high-impact model use cases
Build end-to-end pipelines
Define standardized deployment patterns
Own KTLO for ML and GenAI systems
Design inference pipelines
Establish observability frameworks
Enable deployment of agentic AI solutions
Ensure secure deployment of applications
Drive cost and performance optimization
Conduct research into emerging tools
How You'll Work.
Team & Collaboration
Partner with Data Scientists; Partner with architecture teams; Partner with compliance teams; Partner with governance teams; Partner with legal teams; Cross-functional collaboration
Full Job Description
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. ## **Job Title** Senior MLOps Engineer This position follows our Flex & Connect model of 2x per week onsite in Mississauga, ON. ## **Summary** Join McKesson’s growing AI/ML team and play a critical role in operationalizing machine learning and Generative AI solutions at scale. This role focuses on deploying, standardizing, and maintaining production-ready ML and agentic AI systems—enabling consistent, reliable, and optimized delivery of data science innovations that support McKesson’s AIM28 strategic initiatives. ## **What You’ll Do** * Lead deployment and operationalization of ML models and GenAI/agentic solutions, ensuring scalability, reliability, and performance * Partner with Data Scientists to identify and automate high-impact model use cases, building end-to-end pipelines (CI/CD, monitoring, alerting) * Define and enforce standardized deployment patterns and runbooks across teams * Own KTLO (keep-the-lights-on) operations for ML and GenAI systems including health monitoring, logging, and performance tracking * Design and implement pipelines for batch, real-time, and event-driven inference * Establish observability frameworks (monitoring, logging, lineage, alerting) * Enable deployment of agentic AI solutions using tools such as LangChain, LangGraph, Semantic Kernel, and Databricks tools * Ensure secure deployment of applications with proper access controls (
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