Platform Engineer Intern

About the Role

Host360 is building an AI infrastructure platform to transform GPU servers into a scalable, multi-tenant AI cloud. The goal is to enable customers to share GPU resources efficiently, deploy AI models, serve inference requests, and manage AI workloads through a unified platform. This is a paid internship opportunity for exceptionally curious engineering students and fresh graduates interested in building things, experimenting with open-source technologies, and solving challenging systems problems.

• Duration: 6-12 Months
• Type: Paid Internship
• Location: Remote
• Opportunity for Full-Time: Yes (Based on Performance)
• Stipend: Up to 12K INR

What You Will Work On

• GPU sharing and slicing: Explore GPU partitioning, GPU time-slicing, resource isolation, and efficient allocation of GPU resources.
• 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.
• Research and experimentation: Evaluate open-source projects, understand their internals, run experiments, and create prototypes.

Requirements & Tech Stack

• Strong programming fundamentals in Go and Python.
• Familiarity with containers, Docker, or Kubernetes through coursework, projects, or self-learning.
• A good understanding of operating systems, Linux, networking, and basic computer architecture.
• 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 (e.g., GitHub projects, technical experiments, open-source contributions, personal prototypes).

Good to Have (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.

Who Should Apply

• Students in their pre-final or final year of Computer Science, IT, Electronics, or related engineering disciplines.
• Recent graduates who enjoy systems programming and infrastructure engineering.
• Self-taught engineers and candidates from non-traditional backgrounds who can demonstrate skills through practical projects.

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.