Lead AI Platform & Automation Engineer

About the job

Before you apply to a job, select your language preference from the options available at the top right of this page. Explore your next opportunity at a Fortune Global 500 organization. Envision innovative possibilities, experience our rewarding culture, and work with talented teams that help you become better every day. We know what it takes to lead UPS into tomorrow—people with a unique combinat ... ion of skill + passion. If you have the qualities and drive to lead yourself or teams, there are roles ready to cultivate your skills and take you to the next level. Job Description: Job Summary We are looking for a strategic and hands-on Lead AI Platform & Automation Engineer to own and scale our enterprise AI platform capabilities. This role will serve as the primary owner of the IBM watsonx platform and Google Cloud Vertex AI platform, while providing support to GCP driving adoption, standardization, automation, and governance across the organization. The role also includes leading a team responsible for day-to-day AI model and application onboarding, pipeline automation, DevSecOps integration, BI tooling, and AI governance—including maintenance of IBM watsonx.governance and vertex AI. This is a high-impact position suited for a technically strong leader who can lead technical team, architect solutions, scale platforms, and align multiple teams across engineering, data science, and business domains. Key Responsibilities Platform Leadership • Serve as the lead owner of IBM watsonx and Google Vertex AI platforms, overseeing configuration, governance, and operational maturity. • Drive platform onboarding strategy, enablement, and hands-on support across data science, engineering, and analytics teams. • Standardize reusable frameworks, templates, and infrastructure patterns for both platforms to accelerate project delivery. AI & ML Pipeline Automation • Architect and manage enterprise-wide CI/CD and MLOps pipelines for: • Model training, tuning, evaluation, and deployment • Data ingestion, transformation, and streaming • Infrastructure provisioning and teardown (IaC) • Build and maintain reusable templates for AI applications, workflows, and serving endpoints in IBM watsonx.ai and Vertex AI. Agentic AI Enablement • Design and operationalize pipelines for Agentic AI systems using LLMs, orchestration agents, and decision engines. • Integrate intelligent agent workflows with platform capabilities and monitor lifecycle behavior and governance adherence. AI Governance (incl. watsonx.governance) • Implement and operationalize AI governance frameworks using IBM watsonx.governance. • Define model approval workflows, track metadata, enforce responsible AI policies, and ensure transparency, bias detection, and explainability. • Collaborate with legal, compliance, and information security teams to embed AI governance across all AI/ML systems. DevSecOps & Automation • Integrate DevSecOps practices into pipelines—automating vulnerability scans, access policies, and secrets management. • Embed security compliance and quality gates into all CI/CD workflows across AI and application domains. Business Intelligence & Semantic Modeling • Provide architectural guidance to BI teams using Power BI and Looker. • Oversee data modeling, semantic layer standardization, and connectivity to enterprise data lakes and warehouses. Team Leadership • Lead and mentor a cross-functional team of AI platform engineers, MLOps practitioners, BI analysts, and DevSecOps engineers. • Define goals, delivery milestones, documentation standards, and best practices for scalable operations.