GCP Infrastructure Engineer - GenAI Cloud Platform

About the role

UPS is looking for a highly skilled GCP Infrastructure Engineer to design, build and manage the cloud infrastructure powering Generative AI (GenAI) applications at scale. The engineer uses Google Cloud Platform (GCP) Vertex AI, IBM Watsonx and containerization technologies such as Docker and Kubernetes (GKE) to deliver secure, scalable and high-performance AI solutions, owning the full infrastructure lifecycle from design and provisioning to automation, monitoring and optimization.

Key responsibilities

• Design, provision and maintain scalable, secure and cost-efficient infrastructure for GenAI applications on GCP, including GPU/TPU clusters for distributed training and inference.
• Deploy and manage containerized workloads with Docker and Kubernetes (GKE), and configure Vertex AI and IBM Watsonx for training, fine-tuning and serving LLMs.
• Build and maintain Infrastructure as Code (Terraform or Cloud Deployment Manager) and adopt GitOps (Flux); build and optimize CI/CD pipelines.
• Apply SRE principles (SLIs, SLOs, SLAs) and embed DevSecOps, including policy-as-code, vulnerability scanning, secrets management, IAM, network segmentation and data encryption (HIPAA, SOX, GDPR, FedRAMP).
• Set up observability stacks (Prometheus, Grafana, Cloud Monitoring, Datadog) and define KPIs for system health and adoption.
• Optimize cloud costs for GPU/TPU workloads via autoscaling, preemptible instances and utilization monitoring.
• Partner with data scientists, ML engineers and software teams, and provide onboarding, documentation and reusable templates.

Required profile

• Bachelor's or master's degree in computer science, software engineering or a related field.
• 8+ years of experience in cloud infrastructure, DevOps or platform engineering.
• Experience with GenAI use cases (chatbots, content generation, code assistants).
• Preferred: MLOps experience, multi-cloud or hybrid cloud exposure, feature stores and ML observability tools, distributed training frameworks, open-source contributions and regulated-industry experience.

Required skills

• Google Cloud Platform (GCP), especially Vertex AI, and IBM Watsonx.
• Docker, Kubernetes (GKE) and container orchestration at scale.
• Python, Bash or similar scripting languages.
• CI/CD tools (GitHub Actions, GitLab CI, Jenkins) and IaC tools (Terraform, Pulumi, Ansible, Deployment Manager).
• Data pipeline tools (Dataflow, Apache Beam, Pub/Sub, Kafka).
• Preferred certifications: Google Cloud Professional Cloud Architect, Machine Learning Engineer, CKA/CKAD, IBM Watsonx Generative AI Engineer, Cloud Pak for Data.