Collaborate with data scientists, machine learning engineers, and analytics teams to provide technical direction for AI and advanced analytics platforms
Work closely with data warehousing, data engineering, and cloud platform teams to design optimal architectures for AI-driven data solutions
Enable the scalable use of AI-generated outputs (e.g., ML predictions, extracted signals, model outputs) in conjunction with structured data to support analytics and oncology insights
Partner with senior management and stakeholders to communicate AI system capabilities, implementation approaches, assumptions, and limitations in clear, non-technical language
Participate in the full lifecycle of AI and data platform solutions, including planning, design, implementation, deployment, monitoring, and ongoing maintenance
Design, build, and maintain production-grade AI pipelines, shared frameworks, and supporting services in the cloud (e.g., AWS, GCP, Azure; Azure preferred)
Design, test, and maintain AI-enabled applications and services using modern software engineering and testing methodologies
Perform code reviews and help define engineering and AI code standards to ensure high-quality, scalable, and maintainable solutions
Develop and maintain scalable data and AI pipelines using Python and supporting technologies
Design and implement data architectures that support downstream analytics and access by McKesson analysts and AI data consumers
Drive innovation and develop reusable engineering solutions to support AI workloads, model execution, inference pipelines, and integration into downstream data products
Evaluate new AI-related tools, frameworks, and platforms to improve scalability, reliability, and developer productivity prior to broader adoption
Requirements:
Degree or equivalent and typically requires 7+ years of relevant experience
3+ years of relevant experience in data engineering or software development roles supporting analytics or AI-enabled solutions
Proficiency in Python and SQL
Demonstrated experience developing and maintaining reliable, production-grade data pipelines and analytical datasets
Experience building and supporting internal tools or applications used for data validation, monitoring, review, or operational analytics workflows
Working knowledge of application integration patterns, including service-based architectures and data access layers that support UI-driven tools
Hands-on experience using Databricks for data processing, analytics development, and collaboration with data science or analytics teams
Experience working within Microsoft Azure environments, applying standard engineering practices to deliver maintainable, well-documented solutions