Cloud AI Engineer, Lead

Overview

The Cloud AI Engineer, Lead at Zebra is a critical role in driving innovation across IT Platform Engineering, Cloud Security, and Artificial Intelligence initiatives. This role focuses on designing, building, and scaling next-generation cloud-native AI platforms, AI-powered log intelligence (AIOps), and semantic developer tooling.

Essential Duties/Responsibilities

• Identify and evaluate emerging innovation opportunities across Platform Engineering and Cloud Infrastructure functions, matching business needs with cutting-edge technology solutions.
• Conduct comprehensive business and systems analyses, translating complex technical findings into clear user stories, system requirements, and actionable solution concepts.
• Cook up and execute Proof of Concepts (PoCs) and hands-on delivery activities to rapidly validate and advance cloud and platform innovation initiatives.
• Research, analyze, and report on disruptive technologies—such as Generative AI, Machine Learning (ML), Robotic Process Automation (RPA), and Virtual Agents—providing strategic recommendations for corporate adoption.
• Act as the primary IT Cloud Innovation Liaison, driving cross-functional collaboration and alignment with other innovation and product teams across Zebra.
• Serve as an active Innovation Evangelist within IT and the broader enterprise, promoting modern engineering methodologies and pushing the boundaries of cloud-native possibilities.
• Track, analyze, and report key performance indicators (KPIs), ensuring leadership has complete visibility into the progress and business outcomes of all innovation initiatives.
• Establish and maintain a repeatable operating model for cloud-native AI/ML deployment, ensuring all initiatives consistently address pipeline architecture, performance metrics, and compliance guidelines across GCP and Azure.
• Lead and mentor a high-performing team of cloud and AI platform engineers, guiding their professional evolution toward predictive log analytics, automation (AIOps), and robust cloud data readiness.
• Translate complex AI governance, cloud security guardrails, and regulatory requirements into practical technical specifications, partnering directly with systems engineers to integrate PII masking and RBAC into automated CI/CD pipelines.
• Manage roadmap alignment within assigned technology portfolios.

Minimum Qualifications

• Bachelor's degree in Computer Science, Electronic Engineering, Computer Engineering, or related field.
• 5+ years experience working on an operations style team (NOC, SOC, MOC, etc.) and troubleshooting networking, service desk, operations center and/or supporting cloud based Infrastructure.
• Hands-on Google Gemini Enterprise Expertise: Demonstrated experience working with Google Gemini Enterprise, with a proven ability to build and deploy custom agents using the Agent Development Kit (ADK).
• Multi-Cloud & GenAI Platform Proficiency: Deep familiarity with leading cloud platforms and generative AI solution providers, including Google Cloud Platform (GCP), Azure, OpenAI, and Anthropic.
• Experience working in a global, heterogeneous, cloud and on-prem environment.
• Practical knowledge of, or deep technical curiosity to develop, hands-on solutions involving Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures, and agentic AI patterns (including Model Context Protocol [MCP], tool calling, and orchestration frameworks).
• Expertise in scripting or programming languages (e.g., Python) for automation or testing is a plus; software development background is welcomed.
• Comprehensive understanding of enterprise cloud security, application/API security, and fine-grained identity systems, with experience integrating PII masking and complex Role-Based Access Control (RBAC) frameworks.
• Strong software development background with advanced scripting capabilities in Python (or similar languages) to drive automation, infrastructure provisioning (IaC), and automated testing.
• Active professional-level cloud certifications are highly preferred, such as Google Cloud Professional Cloud Architect, Google Cloud Professional Machine Learning Engineer or AI Engineer.

Preferred Qualifications

• Some exposure to AI/ML systems, application security, or API security is a plus.
• Travel up to 10%.