Success is measured by the reliability, security, and scalability of the AI systems shipped, the rate at which Senior/Associate engineers adopt the architectural standards this role sets, and this role's influence on AI platform decisions across the organization.
Key Responsibilities
AI Architecture Leadership: Define the technical architecture and roadmap for LLM/agentic systems — model routing across cloud AI platforms, connector/integration patterns, RAG pipelines, and prompt/evaluation frameworks.
Solution Design & Governance: Lead architecture reviews for new AI capabilities and ensure designs meet enterprise security, privacy, and data-provenance requirements.
Platform Ownership: Drive the technical design of shared AI platform capabilities, including knowledge-retrieval systems, citation/traceability of AI-generated outputs, and permission-aware access to data.
Hands-On Delivery: Personally architect and build high-complexity components on priority AI delivery initiatives where a working system, not just a design doc, is the deliverable.
Technical Mentorship: Set coding, architecture, and evaluation standards for Senior and Associate AI Engineers; run design reviews and pair on complex agentic workflows.
LLMOps & Responsible AI: Establish secure, observable, and cost-attributed AI operations — usage/spend attribution, abuse monitoring, model evaluation, and rollback strategy — for production LLM systems.
Cross-Functional Architecture: Partner with engineering managers, product, platform, and Legal/Compliance to align AI architecture decisions with delivery timelines and enterprise standards.
Vendor & Model Strategy: Evaluate foundation model providers and AI tooling vendors; make build-vs-buy and model-selection recommendations.
Compliance & Risk: Ensure AI systems handling protected health information (PHI) meet regulatory and enterprise governance requirements.
Organizational Impact: Represent AI architecture in cross-company forums and influence enterprise-wide AI platform direction.
Priority will be given to candidates who reside in the Columbus, OH metropolitan area.
We are unable to provide sponsorship for this role, presently or in the future.
Minimum Requirements
Bachelor's degree or equivalent experience; typically 8+ years of software engineering experience, including 3+ years focused on AI/ML, LLM, or NLP systems in production. Fewer years required with a relevant Master's or Doctorate.
Critical Skills
One level of demonstrated technical leadership above a Senior AI Engineer — e.g., owning architecture for a team or platform area, not just a single feature.
Deep, hands-on expertise building production LLM applications: prompt engineering, RAG/GraphRAG, agentic workflows, tool use, and evaluation harnesses.
Experience with commercial large language model APIs (e.g., Anthropic, OpenAI) and tool-integration protocols such as Model Context Protocol (MCP).
Strong software engineering fundamentals: Python and/or Java, REST APIs, event-driven architecture, microservices, and cloud-native design (Azure preferred; AWS/GCP acceptable).
Experience with vector/graph retrieval systems, embeddings, and knowledge-base architectures at production scale.
Working knowledge of LLMOps/MLOps: usage attribution, cost/spend monitoring, abuse detection, and model evaluation pipelines.
Ability to set architectural standards and mentor other engineers through design review and pairing, not just documentation.
Familiarity with regulated/healthcare data environments and AI governance frameworks (model risk review, connector approval tiers, PHI handling) is a plus.
Preferred Qualifications
Advanced degree (M.S. or higher) in Computer Science, AI/ML, or a related technical field.
Prior experience leading AI/ML platform or applied-AI teams inside healthcare IT or another highly regulated industry.
Experience modernizing legacy systems toward AI-embedded workflows (agent-assisted development, AI-powered risk scoring, AI-driven automation).
Experience with Confluence/Jira-based engineering governance and enterprise architecture review boards.
Additional Skills
Strong awareness of the fast-moving GenAI/agentic landscape and a demonstrated habit of continuous learning.
Experience managing relationships with AI tooling vendors and model providers.
Clear written and verbal communication for both technical and executive audiences
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
$155,300 - $258,800
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