AI Infrastructure Architect

About the Role

We are looking for a Systems or Solutions Architect with deep expertise in networking, infrastructure-as-a-service (IaaS), and cloud-scale system design to help architect and optimize AI / ML infrastructure .

The ideal candidate combines strong fundamentals in cloud architecture (AWS or equivalent) , networking , compute , and storage , with hands-on experience in Kubernetes, observability, and automation .

You’ll design scalable systems that support large AI workloads — enabling efficient training, inference, and data pipelines across distributed environments.

Key Responsibilities

• Architect and scale AI / ML infrastructure across public cloud (AWS / Azure / GCP) and hybrid environments.
• Design and optimize compute, storage, and network topologies for distributed training and inference clusters.
• Build and manage containerized environments using Kubernetes, Docker, and Helm .
• Develop automation frameworks for provisioning, scaling, and monitoring infrastructure using Python, Go, and IaC (Terraform / CloudFormation) .
• Partner with data science and ML Ops teams to align AI infrastructure requirements (GPU / CPU scaling, caching, throughput, latency).
• Implement observability, logging, and tracing using Prometheus, Grafana, CloudWatch, or Open Telemetry .
• Drive networking automation (BGP, routing, load balancing, VPNs, service meshes) using software-defined networking (SDN) and modern APIs.
• Lead performance, reliability, and cost-optimization efforts for AI training and inference pipelines.
• Collaborate cross-functionally with product, platform, and operations teams to ensure secure, performant, and resilient infrastructure .

Required Qualifications

Preferred Qualifications