Infrastructure Engineer

About aion

Aion is the enterprise AI platform, a full-stack solution for building, fine-tuning, and deploying AI at scale. Whether an organization is modernizing internal operations, launching AI-powered products, or transforming customer experiences, Aion takes them from concept to production on a single, unified platform.

We work differently than most AI companies: our teams deploy alongside our customers, turning production-ready AI into real business outcomes in weeks, not quarters.

We’re a fast-growing, VC-backed startup led by founders with a track record of successful exits. With teams across the US, UK, and India, we’re building the next generation of enterprise AI and we’re looking for exceptional people to help us scale.

Who You Are

You are a visionary infrastructure architect passionate about democratizing AI compute at global scale. You thrive on solving complex technical challenges that create elegant, accessible systems from intricate infrastructure. With deep expertise in secure multi-tenancy environments, you understand how to design and implement comprehensive isolation guarantees across hardware, network, and storage layers for both VM and container workloads.

You're excited to join an ambitious AI infrastructure startup at the ground floor, where your work will directly unlock siloed compute resources and remove barriers limiting AI advancement. You have the technical depth to architect platform systems that seamlessly connect compute providers with AI engineers while maintaining robust security foundations that scale to serve diverse client requirements and compliance needs.

You're motivated by the opportunity to build something transformative—creating the infrastructure that will make high-performance compute more accessible, affordable, and user-friendly for the next generation of AI innovation.

What You'll Do

• Observability Systems: Build and deploy comprehensive monitoring for GPU infrastructure using DCGM, NVML, and custom exporters; design metrics collection pipelines that track GPU health, utilization, thermal management, and performance across heterogeneous providers
• LGTM Stack Architecture & Operations: Deploy and manage production-scale Loki, Grafana, Tempo, and Mimir alongside Prometheus and Thanos/VictoriaMetrics; design retention strategies, aggregation rules, and query patterns that scale to thousands of GPUs
• Custom Exporter Development: Write Prometheus exporters in Go or Python for GPU metrics, platform services, and infrastructure components; implement proper metric naming, labeling strategies, and follow OpenMetrics standards
• Kubernetes Controller Development: Build custom controllers and operators for GPU workload management, scheduling, and resource allocation; instrument controllers with comprehensive metrics and tracing for observability