Modern platforms

AI & Modern Infrastructure

Production paths for inference and retrieval: GPU sizing, serving stacks, vector search and observability tuned for latency and cost.

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AI stack

AI & Modern Infrastructure Coverage

GPU compute, inference serving, vector search and production platform patterns — broken down by area with concrete outcomes.

Compute platform

GPU Infrastructure

Problems solved

  • GPU instances sized for experiments but left running in production
  • No failover or queue strategy when GPU capacity is saturated
  • Cloud GPU costs opaque to product and platform teams
  • Driver, CUDA and node image drift across environments
  • Scaling plans that ignore cold-start and provisioning latency

Technologies

  • AWS GPU instances
  • Azure NC-series
  • GCP A100 / L4
  • Node pools
  • Spot & reserved GPU
  • Capacity planning

Outcomes

  • Right-sized GPU capacity
  • Predictable inference throughput
  • Lower idle GPU spend
  • Consistent GPU node operations

Inference stack

LLM Inference & vLLM

Problems solved

  • Model serving latency spiking under concurrent requests
  • No autoscaling or batching strategy for inference workloads
  • Model versions promoted without canary or rollback paths
  • Token and request metrics missing from operational dashboards
  • Inference stacks chosen without cost-per-request analysis

Technologies

  • vLLM
  • TGI
  • FastAPI
  • Model routing
  • Autoscaling
  • Canary deployments

Outcomes

  • Stable inference latency
  • Safer model rollouts
  • Cost-aware serving architecture
  • Observable request and token metrics

Platform engineering

Production AI Platform

Problems solved

  • AI features shipped without SLOs, quotas or abuse controls
  • Secrets, API keys and model endpoints managed ad hoc
  • Observability focused on infra, not model or retrieval failures
  • Platform teams blocked by fragmented tooling across teams
  • Cost of inference and storage disconnected from product usage

Technologies

  • FastAPI
  • Kubernetes
  • OpenTelemetry
  • Rate limiting
  • Secrets management
  • Cost attribution

Outcomes

  • Production-ready AI services
  • Clear platform ownership
  • End-to-end AI observability
  • Sustainable inference economics

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