Platform Engineer Intern - GPU Infrastructure & AI Inference

📍Host360 | Platform Engineering Team For engineers who love understanding how modern infrastructure actually works - not just using it. **Job Title** : Platform Engineer Intern - GPU Infrastructure & AI Inference **Company** : Host360 **Location** : Remote **Type** : Paid Internship **Duration** : 6-12 Months **Opportunity for Full-Time** : Yes (Based on Performance) **Stipend** - Upto 12K INR **Role Description** At **Host360** , we are building an AI infrastructure platform that transforms GPU servers into a scalable, multi-tenant AI cloud. Our goal is to enable customers to share GPU resources efficiently, deploy AI models, serve inference requests and manage AI workloads through a unified platform. We are looking for exceptionally curious engineering students and fresh graduates who enjoy building things, experimenting with open-source technologies and solving challenging systems problems. This is an opportunity to work on real infrastructure at the intersection of **GPU virtualization, Kubernetes, distributed computing and large language model (LLM) inference** . **What you will work on** * **GPU sharing and slicing:** Explore GPU partitioning, GPU time-slicing, resource isolation and efficient allocation of GPU resources across multiple workloads. * **Kubernetes-based AI infrastructure:** Work with Kubernetes, GPU device plugins, NVIDIA GPU Operator and related cloud-native technologies. * **AI inference systems:** Deploy and benchmark open-source inference engines such as vLLM, NVIDIA Triton Inference Server or SGLang. * **Distributed AI workloads:** Experiment with Ray and KubeRay for distributed execution, workload orchestration and scalable model serving. * **Performance engineering:** Measure inference latency, throughput, GPU utilization, VRAM consumption and cost per inference request. * **Platform engineering:** Help build automation, APIs, monitoring and control-plane components that make AI infrastructure easy to operate. **Research and experimentation:** Evaluate open-source projects, understand their internals, run experiments and turn promising ideas into working prototypes. **What we are looking for** We care more about your ability to learn and build than the number of technologies listed on your resume. * Strong programming fundamentals in Go and Python. * Exposure to Hands-on exposure to Kserve, Kubernetes, KubeRay, Ray Serve or distributed computing. * A good understanding of operating systems, Linux, networking and basic computer architecture. * Familiarity with containers, Docker or Kubernetes through coursework, projects or self-learning. * Genuine curiosity about GPUs, parallel computing, AI infrastructure or distributed systems. * Ability to read technical documentation, explore unfamiliar codebases and debug problems independently. * Evidence of building something beyond classroom assignments — GitHub projects, technical experiments, open-source contributions or personal prototypes. **Good to have — but not mandatory** * Hands-on exposure to Kserve, Kubernetes, KubeRay, Ray Serve or distributed computing. * Experience deploying LLMs using vLLM, Triton or SGLang. * Familiarity with NVIDIA CUDA, GPU memory, VRAM utilization or GPU profiling. * Understanding of GPU MIG, time-slicing, device plugins or resource scheduling. * Experience with benchmarking, Linux performance tools or observability systems. **You do not need to know all these technologies before joining.** We are looking for strong fundamentals, demonstrated curiosity and the ability to learn quickly. **What you will gain** * Hands-on experience building a real AI infrastructure product. * Exposure to GPU resource management, model serving and cloud-native systems. * The opportunity to experiment with leading open-source projects and contribute working code. * Mentorship from engineers building cloud and AI infrastructure. * The possibility of a full-time engineering opportunity based on performance and business requirements. **Who should apply?** Students in their pre-final or final year of Computer Science, IT, Electronics or related engineering disciplines, as well as recent graduates who enjoy systems programming and infrastructure engineering. We welcome self-taught engineers and candidates from non-traditional backgrounds who can demonstrate their skills through practical projects. The goal is not simply to complete an internship. The goal is to build engineers!! **If you’re looking for a role where thinking matters more than titles, we want to talk.** **#How We Evaluate You** * How you think, not where you worked * How you write, not what tools you list * How you reason through complexity 📩 Apply only if you enjoy deep products and hard problems.