Senior Engineer - AI Platforms

Cardinal Health
United States
3 months ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
8 years minimum
Compensation
$123,400.0 - $176,300.0
Working hours
Regular working hours
Job source

Tech stack

Java (Programming Language) Artificial Intelligence Automation of Tests Bash Shell Code Review Continuous Integration Identity and Access Management Python (Programming Language) Platform as a Service (PAAS) Azure Machine Learning Salesforce.Com Software Engineering
+18 more
Management of Software Versions Enterprise Search Rust (Programming Language) Dynamic Routing Google Cloud Large Language Models Generative AI Amazon Virtual Private Cloud (VPC) AI Platforms Kubernetes Infrastructure Automation Frameworks Deployment Automation Machine Learning Operations Virtual Agents Terraform Network Server Multiplatform Servicenow

Job description

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., 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.

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.

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.

Benefits & conditions

Paid parental leave, Parental leave, 401(k), Health insurance, Paid time off, Vision insurance, Health savings account, Dental insurance, Anticipated salary range: $123,400 - $176,300

Bonus eligible: yes

Benefits: Cardinal Health offers a wide variety of benefits and programs to support health and well-being.

  • Medical, dental and vision coverage
  • Paid time off plan
  • Health savings account (HSA)
  • 401k savings plan
  • Access to wages before pay day with myFlexPay
  • Flexible spending accounts (FSAs)
  • Short- and long-term disability coverage
  • Work-Life resources
  • Paid parental leave
  • Healthy lifestyle programs

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on indeed.com

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

1:34 min

Essential commands for running and testing Terraform configurations

Hennie Francis · LIVE

56 sec

Integrating automated approval workflows into the portal

Markus Eisele Markus Eisele · WWC 2025

3:52 min

Avoiding remote code execution from unsanitized inputs

Alexander Pirker · WWC 2022

3:03 min

Career evolution in data engineering and AI platforms

Maria Apazoglou · Coffee With Developers

2:32 min

Overview of Terraform and Terraform Cloud features

Devlin Duldulao · LIVE

3:45 min

Fusing developer experience and platform engineering for agentic SDLC

Julia Kordick Julia Kordick · WWC Europe 2026

Videos

See all

Related articles

See all