AI/ML Lead (Contract)

Single-handedly revolutionized a startup's AI capabilities, scaling from nascent to millions of users.

Platform & Orchestration

• Fleet rebuild: from a handful of Azure VM GPUs to a fully orchestrated, hyperscale GKE on GCP environment with NVIDIA GPU autoscaling, supporting multi-GPU and diverse generative workloads
• CloudNative IaC discipline: Terraform, Helm, ArgoCD, and GitOps
• Just-in-time launch: delivered the GKE fleet ahead of media and TV exposure — fleet held the resulting ramp from hundreds of thousands to millions of users at 99.99% uptime
• Dev → prod gating: containerized AI workflows and inference for consistent deployment and controlled promotion across environments

Inference & Generative Workloads

• Data pipelines: engineered the foundations that fed training and inference workflows
• 'SuperBooga' inference broker: an event-driven bus fronting GPU inference — nodes pull from a pub/sub queue and serve one request at a time per GPU, avoiding the performance hit a single GPU takes from concurrent inference
• GPU & instance right-sizing: through performance and load-testing, matched adequate GPU and instance family to each workload while keeping a cost-efficient footprint
• Distributed weights & adapters: intra-cluster storage kept in sync with the upstream source and fanned out to inference containers on demand, accelerating cold-start times and cutting storage costs

ML Engineering & Practice

• End-to-end product delivery: translated aspirational & conceptual product requirements into shipped features — both the supporting software and custom-trained PyTorch/Transformers models
• Performance discipline: custom stress-testing for LLM and Stable Diffusion, anchored in a K6-driven load-testing and observability culture