Sr. Machine Learning Ops Engineer

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 (e.g., Okta integration)
• Drive cost and performance optimization across ML and GenAI workloads
• Partner with architecture, compliance, governance, and legal teams to meet enterprise standards
• Conduct ongoing research into emerging tools and technologies to improve deployment practices
• Guide and influence architectural decisions while maintaining clear separation between platform and deployment ownership

What You Bring

• Strong 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 (LangChain, Semantic Kernel, etc.)
• 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)
• Strong cross-functional collaboration skills (Data Science, Product, Architecture)
• Ability to drive standardization, automation, and platform maturity
• Focus on reliability, scalability, and optimization

Minimum Requirements

• Degree or equivalent and typically requires 7+ years of relevant experience.

Preferable Skills & Experience

• 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

We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please

Our Base Pay Range for this position

$99,100 - $132,100

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McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application.

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