Agent lifecycle management: Support the design, deployment, versioning, and retirement of AI agents, skills, MCP servers, and workflows across business units.
Tool and data integration: Enable agents to securely use approved tools, APIs, and governed enterprise data sources, and integrate with business applications such as SAP, Power Apps, and Microsoft 365.
Monitoring and observability: Implement and maintain telemetry, logging, evaluation, and alerting to track agent performance, reliability, quality, and usage.
Production operations: Triage and resolve incidents and tickets, perform root cause analysis, and manage releases, change requests, and post-production support.
Governance and compliance: Apply access controls, privacy, security, and responsible AI guardrails; support architecture and governance review boards and audit requirements.
Cost and capacity management: Partner with AI FinOps and Cloud FinOps teams to monitor consumption, optimize spend, and forecast capacity.
Enablement and adoption: Onboard teams, document standards and best practices, and reduce setup friction so business teams can realize value faster.
Collaboration: Work with engineering, architecture, security, data, product, and business stakeholders to translate use cases into reliable, production-ready AI capabilities.
Basic Requirements
Bachelor's degree in computer science, Information Systems, Engineering, or equivalent practical experience.
5+ years of experience in platform administration, DevOps, cloud operations, or AI/ML operations, with recent hands-on exposure to agentic AI or generative AI solutions.
Working knowledge of cloud AI platforms such as Azure AI Foundry, Microsoft Copilot ecosystem, and related services.
Experience with APIs, identity and access management, and secure integration of enterprise applications and data.
Familiarity with LLM concepts: prompts, retrieval-augmented generation, tool calling, agent orchestration, and evaluation.
Experience with incident, problem, and change management processes in a production environment.
Understanding of security, privacy, compliance, and responsible AI principles.
Strong communication skills, with the ability to explain technical topics to business and leadership audiences.
Preferred Skills/Experience
Experience with Model Context Protocol (MCP), agent frameworks, and agent discovery or registry models.
Experience with Python, PowerShell, or scripting for automation and infrastructure-as-code.
Exposure to SAP S/4HANA Fiori, Power Platform, or other enterprise business applications.
Knowledge of AI FinOps practices, including usage-based cost tracking and budget reporting.
Experience in healthcare, pharmaceutical, or other regulated industries.
Relevant certifications in Azure, AI, security, or ITIL.
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 click here.
Our Base Pay Range for this position
$99,100 - $132,100
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