Senior Engineer - AI Platforms

Cardinal Health

About The Position

AI Platform Tech Lead AI Platform Engineering at Cardinal Health delivers the enterprise-grade foundation for agentic intelligence. We provide the shared infrastructure, automated guardrails, and self-service patterns that empower teams to transition AI agents from prototype to production with velocity and safety. By standardizing the Agentic Development Life Cycle (ADLC), our platform eliminates friction in multi-platform orchestration, MCP-based tool integration, and automated security perimeters, ensuring that every AI solution is secure-by-design and operationally transparent. Job Summary The AI Platform Tech Lead (P4) is a hands-on technical leader responsible for the technical strategy, architecture, and delivery of key AI platform capabilities, including Generative and Agentic AI. This role guides reusable patterns and technology architecture, drives adoption of next-generation platforms, and reduces complexity while increasing business value. The Tech Lead partners closely with engineering managers and stakeholders to translate requirements into a practical technical roadmap and leads a small pod/team through execution with a strong focus on reliability, security-by-design, and developer experience. What Is Expected of You and Others at This Level Serve as a hands-on technical leader who sets direction for designs and technology architecture and drives adoption of modern patterns. Mentor and level-up engineers through coaching, code review, and reusable best practices. Deliver scalable platform capabilities that standardize the AI lifecycle and improve speed-to-production with embedded guardrails.

Requirements

8+ years of Infrastructure Platform Management experience, including experience leading technical design and delivery for application or AI/ML platforms preffered.
Demonstrated competency of the Agent Development Kit (ADK) and orchestration patterns like sequential, parallel, and dynamic routing.
Understanding of the Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols for cross-platform interoperability (e.g., Salesforce Agentforce, ServiceNow).
Strong proficiency in Python or another language (e.g., Go, Java, Rust, Bash).
Experience with infrastructure automation (Terraform or similar)
Strong mastery of Google Cloud Platform for CaaS or PaaS workloads, including VPC Service Controls for protection of sensitive data
Demonstrated ability to guide architecture, produce estimates, and execute implementations while minimizing risk to production systems.

Responsibilities

Lead the design and implementation of a unified AI platform, assisting in critical build-versus-buy recommendations for components such as Agent Engines, MCP Servers, AI enabled Enterprise Search, and Agentic Orchestration
Provide options analysis and estimates based on high-level requirements; drive technical direction for platform designs and technology architecture.
Define and standardize “paved road” patterns that accelerate product teams from experimentation to production.
Design and scale Kubernetes-based compute optimized for AI workloads
Establish MLOps lifecycle automation including CI/CD for models/services, automated testing, versioning, and deployment strategies (e.g., canary/A-B).
Build and improve underlying platform tools to reduce lead time and improve developer usability and consistency across teams.
Embed “security-by-design” guardrails into the platform, including least-privilege IAM models, automated guardrails, and compliance monitoring for AI data privacy.
Design for reliability and ensure stable operations through monitoring, troubleshooting, and continuous improvement, support incident response practices and long-term remediation.
Design and implement the ADLC (Agentic Development Life Cycle) process to register all agents and tools
Design and implement automated governance processes to secure agents, MCP servers, and LLMs.
Act as a coach/mentor to engineers through high-standard code reviews, best practices, and technical guidance.
Partner with engineering management and stakeholders to translate requirements into technical roadmaps and serve as a bridge between data science teams and core infrastructure.