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