Location: Remote (Global) Type: Full-time Company: Yotta Labs Apply: careers@yottalabs.ai 🧠 About Yotta Labs Yotta Labs is pioneering the development of a Decentralized Operating System (DeOS) for AI workload orchestration at a planetary scale. Our mission is to democratize access to AI resources by aggregating geo-distributed GPUs, enabling high-performance computing for AI training and inference on a wide spectrum of hardware—from commodity to high-end GPUs. Our platform supports major large language models (LLMs) and offers customizable solutions for new models, facilitating elastic and efficient AI development. 🛠️ Role Overview We are seeking a GPU Cloud Platform Engineer to join our core infrastructure team and help build the next-generation AI compute cloud. In this role, you will design, deploy, and operate large-scale, multi-cluster GPU infrastructure across data centers and cloud environments. You will be responsible for ensuring high availability, performance, and efficiency of containerized AI workloads—ranging from LLMs to generative models—deployed in Kubernetes-based GPU clusters. If you're passionate about high-performance systems, distributed orchestration, and scaling real-world AI infrastructure, this role offers a unique opportunity to shape the backbone of our AI cloud platform. 🎯 Responsibilities Build and operate large-scale, high-performance GPU clusters; ensure stable operation of compute, network, and storage systems; monitor and troubleshoot online issues. Conduct performance testing and evaluation of multi-node GPU clusters using standard benchmarking tools to identify and resolve performance bottlenecks. Deploy and orchestrate large models (e.g., LLMs, video generation models) across multi-cluster environments using Kubernetes; implement elastic scaling and cross-cluster load balancing to ensure efficient service response under high concurrency for global users. Participate in the design, development, and iteration of GPU cluster scheduling and optimization systems. Define and lead Kubernetes multi-cluster configuration standards; Optimize scheduling strategies (e.g., node affinity, taints/tolerations) to improve GPU resource utilization. Build a unified multi-cluster management and monitoring system to support cross-region resource monitoring, traffic scheduling, and fault failover. Collect key metrics such as GPU memory usage, QPS, and response latency in real time; configure alert mechanisms. Coordinate with IDC providers for planning and deploying large-scale GPU clusters, networks, and storage infrastructure to support internal cloud platforms and external customer needs. ✅ Qualifications

• Designing and deploying AI agents using Google’s Gemini models • Using GCP’s Vertex AI as the primary ML platform (model hosting, pipelines, endpoints) • Building with Google’s agent tooling — the Agent Development Kit (ADK) for constructing agent logic, and the Managed Agents API for running/orchestrating them at scale • Architecting and implementing infrastructure as code (IaC) . • Defining administrative choices for environments that are auto-configured by IaC. • Focus on agent use cases , including relationships with BigQuery databases, enterprise connectors, and agent orchestration. • Heavy leverage of Vertex . • Investigating and tweaking designs to avoid issues with Pfizer’s enterprise infrastructure and GCP limitations.

• Lead the design and architecture of end-to-end integration solutions using Oracle Integration Cloud (OIC), including app-driven orchestrations, scheduled integrations, file-based integrations, event-driven architectures, REST/SOAP APIs, and B2B integrations. • Define integration standards, reference architectures, governance models, reusable patterns, and best practices to ensure consistency, scalability, and maintainability across the enterprise. • Collaborate with business stakeholders, solution architects, and technical teams to gather requirements, analyze integration needs, perform gap analysis, and translate business processes into robust technical designs. • Architect and implement complex integrations, data mappings, transformations (using XSLT, lookups, and canonical models), adapters, and packages in OIC. • Oversee integration with Oracle SaaS applications, legacy systems (including Oracle E-Business Suite), and external platforms, ensuring data integrity, security, and performance. • Provide technical leadership on migration strategies from on-premises middleware (e.g., SOA) to OIC, hybrid cloud integrations, and optimization of existing integration landscapes. • Conduct design reviews, mentor development teams, troubleshoot complex issues, and support testing, deployment, and post-production monitoring. • Stay current with Oracle Integration Cloud updates, new features (such as enhanced observability, projects, file server capabilities, AI agents, and process improvements in recent releases), and industry trends to recommend proactive enhancements. • Ensure compliance with security protocols, data governance, and regulatory requirements in all integration designs.

• Enhance and optimize our API platform using ApigeeX and Google Cloud services • Develop features and automation for APIs and platform components • Implement Infrastructure-as-Code (Terraform, Github Actions) for deployments and provisioning • Ensure API security, compliance, and observability • Designing technical solutions, building, testing and implementing new business requirements • Working in a small and close-knit team who supports each other and shares knowledge actively • Being responsible and guarding the service you build