Cloud AI Engineer, Lead

Zebra Technologies•Holtsville, IL

4d•Hybrid

About The Position

The Cloud AI Engineer, Lead plays a critical role in driving innovation across Zebra’s IT Platform Engineering, Cloud Security, and Artificial Intelligence initiatives. Acting as a hands-on technical architect and strategic trailblazer, you will partner with key stakeholders to identify opportunities, translate ideas into actionable solutions, and deliver value through rapid proofs of concept. This role focuses heavily on designing, building, and scaling our next-generation cloud-native AI platforms, AI-powered log intelligence (AIOps), and semantic developer tooling.

Requirements

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.
Proven track record of deploying containerized AI solutions using Kubernetes (GKE/AKS), Docker, and robust CI/CD pipelines, backed by verifiable metrics such as high availability, latency reduction, and infrastructure cost optimization.
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.

Nice To Haves

Some exposure to AI/ML systems, application security, or API security is a plus.
Active professional-level cloud certifications are highly preferred, such as Google Cloud Professional Cloud Architect, Google Cloud Professional Machine Learning Engineer or AI Engineer

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.
Coordinate 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.
Plan, coordinate, and facilitate structured ideation workshops, fostering a culture of collaborative brainstorming and innovative problem-solving.
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.