A hands-on guide to building a custom gradient checkpointing engine in PyTorch. This side project demonstrates deep memory management and autograd mastery, making your CV stand out to AI infrastructure roles.
AI workloads pushed the network to its limits. This post walks through the protocols, topologies, and failure modes that define modern AI networking — from RoCE and collective operations to fat-tree fabrics and packet-level telemetry.
AI infrastructure is no longer a subfield of MLOps — it is the convergence of six traditional disciplines. Here is how Terraform, Kubernetes, GPU scheduling, inference routing, SRE, and FinOps are collapsing into a single operating model.
AI infrastructure hiring is converging into a single, hybrid discipline that blends platform engineering, MLOps, and distributed systems. This post breaks down what skills are merging, what the new job descriptions look like, and how engineers can position themselves.
We follow a single agent request from the user prompt down through the AI gateway, inference server, GPU/Kubernetes scheduler, and east-west networking — then turn around and look at how observability, security, and FinOps wrap around the whole thing.