Cloud Platform Software Engineer - AI Infrastructure

About us MangoBoost, Inc. Agentic AI is fundamentally reshaping the paradigm of AI infrastructure. As environments emerge where countless AI agents operate simultaneously, conventional data center architectures are reaching their limits in both performance and efficiency. In response to this shift, MangoBoost is pioneering a new approach with its full-stack AI infrastructure solution designed to innovate every layer of the AI data center. From LLMBoost, our AI inference optimization software, to GPU and storage systems powered by our proprietary DPU architecture, and orchestration software that seamlessly integrates and manages the entire stack, MangoBoost designs and develops every layer of AI infrastructure in-house. Rather than building isolated products, we focus on eliminating system-wide bottlenecks and fundamentally addressing inefficiencies across the entire infrastructure stack. MangoBoost’s technology minimizes data movement and coordination overhead while maximizing overall system resource utilization, enabling exceptional performance and optimized total cost of ownership (TCO) for customers. Today, our solutions are already being deployed within real-world AI infrastructure environments through global partners, validating the strength of our technology. Through these efforts, MangoBoost is establishing a new global standard for AI data centers. Position Overview MangoBoost is building a next-generation AI cloud platform that combines hardware accelerators with industry-leading DPU technology. We are evolving into a Cloud Service Provider (CSP) that delivers GPUaaS and LLMaaS based on our AI Cloud Center (DCP). This position plays a pivotal role in designing and developing the software architecture for our virtualization platform and API services, with the goal of stabilizing the platform ahead of full service launch in the second half of 2026. We are looking for a capable cloud engineer who can lead a broad scope of work, from technology stack selection through to service launch. Responsibilities & Opportunities Lead the design of the cloud platform's software architecture and drive technology stack decisions.Participate in development of the virtualization layer and backend for high-performance GPUaaS and LLMaaS (API/SaaS) platforms.Optimize Kubernetes-based orchestration and build multi-tenancy environments to efficiently utilize large-scale GPU/DPU clusters.Design self-healing, failover, and rollback strategies to prepare for large-scale GPU cluster failures.Build an integrated observability system (metrics/logs/traces) for real-time monitoring of GPU/DPU resource status and utilization.Design network, data, and model isolation architecture for multi-tenant environments, and build security frameworks that meet B2B and B2C customer requirements.Participate in designing the billing system and implementing usage-based pricing logic to technically support the business model. Required Qualifications Software-defined mindset, with the ability to control and virtualize infrastructure as code.Practical experience and understanding of containers and virtualization, including Docker, Kubernetes, and KVM.Proficiency in at least one of Go, Python, or Java, with the skill to design scalable systems.Experience designing and improving service operational reliability in large-scale distributed system environments, or experience responding to operational incidents in Kubernetes-based services.Experience building or operating an observability stack such as Open Telemetry.Understanding of resource isolation, security, and access control concepts in multi-tenant environments. Preferred Qualifications Experience with the full zero-to-one lifecycle of GPUaaS or AI API services.Experience optimizing GPU accelerator utilization and troubleshooting large-scale distributed environments.Experience leading the launch and customer engineering(technical support) of a paid B2B SaaS or PaaS product.Experience optimizing complex usage-based billing algorithms and building monitoring systems to detect related issues.Experience developing payment/billing systems where transactional integrity is critical, or large-scale user authentication/authorization management systems.