AI Infrastructure Engineer
Building production AI systems, LLM infrastructure, inference platforms and cloud-native ML solutions.
A practical walkthrough of the infrastructure decisions behind serving large language models reliably — from GPU selection to batching, autoscaling, and observability.
Quantization, batching, and right-sizing strategies that reduced our inference bill by 60% while keeping p99 latency flat.
The core building blocks of a production MLOps platform — model registry, CI/CD for models, and safe rollouts with canaries and shadow deployments.
A high-throughput inference gateway for serving open-weight LLMs with token streaming, request batching, and per-tenant rate limiting. Built to run on Kubernetes with autoscaling backed by GPU node pools.
Reference architecture and Terraform modules for an end-to-end MLOps platform on AWS — feature store, model registry, CI/CD for models, and automated rollout with shadow deployments.
A monitoring stack that attributes GPU utilization and cloud spend to individual models and teams, with Grafana dashboards and Prometheus exporters for inference workloads.