Manager, Data Operations & Management - Vertex AI [T500-29020]

About McDonald’s:

One of the world’s largest employers with locations in more than 100 countries, McDonald’s Corporation has corporate opportunities in Hyderabad. Our global offices serve as dynamic innovation and operations hubs, designed to expand McDonald’s global talent base and in-house expertise. Our new office in Hyderabad will bring together knowledge across business, technology, analytics, and AI, accelerating our ability to deliver impactful solutions for the business and our customers across the globe.

Work location: Hyderabad, India

Work pattern: Full time role.

Work mode: Hybrid.

Position Summary:

Platform Engineer III:

We are seeking an experienced AI/ML Platform Engineer to lead the design, engineering, automation, and governance of enterprise-scale AI/ML platforms on Google Cloud Platform (GCP). The ideal candidate will be responsible for building and evolving secure, scalable, and self-service AI/ML platform capabilities that enable Data Scientists, ML Engineers, and Product Teams to accelerate the development and deployment of AI solutions.

This role requires strong expertise in cloud platform engineering, Infrastructure as Code (IaC), platform reliability, AI/ML ecosystem services, security, governance, and developer experience. The candidate will play a key role in driving platform strategy, architecture standards, automation, and operational excellence across the AI/ML landscape.

Who we’re looking for:

Primary Responsibilities:

AI/ML Platform Architecture & Engineering:

• Design, build, and maintain enterprise-grade AI/ML platform services leveraging Vertex AI and GCP.
• Define platform architecture standards, reusable patterns, and reference implementations for AI/ML workloads.
• Develop scalable self-service capabilities for model development, training, deployment, and lifecycle management.
• Establish platform engineering best practices to improve scalability, availability, security, and operational efficiency.
• Lead platform modernization initiatives and adoption of emerging AI technologies including GenAI and LLM platforms.

Platform Automation & Infrastructure Engineering:

• Build and manage Infrastructure as Code (IaC) solutions using Terraform and cloud-native automation frameworks.
• Automate provisioning of AI/ML environments, networking, security policies, Vertex AI resources, and cloud services.
• Develop reusable automation modules, deployment blueprints, and platform accelerators.
• Drive standardization of cloud resources and platform deployments across multiple environments and business units.

AI/ML Platform Reliability & Operations:

• Define and implement platform reliability, resiliency, disaster recovery, and high-availability strategies.
• Establish SLAs, SLOs, and operational metrics for platform services.
• Build proactive monitoring, observability, alerting, and incident management capabilities.
• Lead root cause analysis and platform optimization initiatives for critical incidents and service disruptions.
• Drive continuous improvement programs focused on platform performance and operational excellence.

CI/CD and Platform Engineering Excellence:

• Design and govern enterprise CI/CD frameworks supporting AI/ML platform services.
• Implement automated testing, validation, security scanning, compliance checks, and release automation.
• Integrate GitHub, GitHub Actions, Cloud Build, Jenkins, SonarQube, and DevSecOps controls into platform pipelines.
• Promote engineering best practices including code quality, versioning, release governance, and platform standards.

Security, Governance & Compliance:

• Implement enterprise security controls, governance frameworks, and policy-driven platform automation.
• Design and manage access controls, IAM policies, service identities, and secrets management.
• Ensure compliance with organizational standards, regulatory requirements, and data governance policies.
• Partner with cybersecurity, architecture, and risk teams to establish secure AI/ML platform foundations.
• Enable Responsible AI, auditability, model governance, and compliance reporting capabilities.

AI Platform Services & Ecosystem Enablement:

• Lead implementation and governance of Vertex AI services including Workbench, Pipelines, Feature Stores, Model Registry, Endpoints, and GenAI capabilities.
• Build and optimize integrations across BigQuery, Cloud Storage, Dataflow, Dataproc, Cloud Run, Cloud …