DevOps Deployment for AI Framework

The immediate goal is to stand up a production-ready environment for a document-grounded AI pipeline. You will take an open-source framework of your choice—whether that ends up being TensorFlow, PyTorch, Hugging Face, or another well-maintained stack—and deploy it so ingestion pipelines, vector search, and model integration all run smoothly under a single, secure umbrella.

Core expectations
• Infrastructure as code so the whole stack can be recreated from scratch in a repeatable way (Terraform or similar).
• Containerised services orchestrated through Kubernetes (managed or self-hosted), with sensible auto-scaling and rolling-update strategies.
• Secure networking, access control, and secrets management baked in from day one.
• Observability: logs, metrics, and alerts wired to a dashboard so issues surface before users notice.
• Performance optimised for low-latency vector similarity queries and efficient document ingestion.

You are free to propose the cloud you feel suits these requirements best (AWS, Google Cloud, Azure, or a hybrid/on-prem alternative). The key criterion is that the final setup can be handed over, documented, and operated by a small in-house team without surprises.

Deliverables
1. IaC repository with all manifests, pipelines, and README.
2. A deployment run-book covering bootstrap, rollback, and scaling procedures.
3. A short live hand-off session (video or recorded) where you walk through the environment, test an end-to-end ingestion → vector index → model query flow, and verify monitoring.