MLOps engineer needed – Dockerize LLM pipeline & deploy to AWS

This is a focused 1-day task. The LLM pipeline already exists in Python — I need it containerized with Docker and running live on AWS by end of day. No research, no exploration — come ready to execute. Deliverables: 1. Dockerfile & Docker Compose Write a production-grade Dockerfile for the LLM inference pipeline. Multi-stage if needed, minimal image size, env vars for config and secrets. 2. AWS deployment Deploy the container to AWS (ECS Fargate preferred, or EC2 if GPU required). Expose a working endpoint. Basic IAM role and security group setup. 3. Smoke test + handoff Confirm the endpoint is live and responding. Provide a short Bash script or README so I can redeploy independently. No undocumented magic. YOU'RE A FIT IF: ✔️ You've deployed a containerized ML or LLM workload to AWS before ✔️ Can work fast without hand-holding ✔️ Comfortable with ECS, EC2, and basic AWS networking ✔️ Available to start immediately SKIP THIS IF: ✘ You've only done local Docker dev ✘ AWS is new to you ✘ You need several days to ramp up ✘ You can't commit to same-day delivery TO APPLY Tell me: (1) one similar project you've shipped — containerized ML/LLM on AWS, (2) your availability to start today, (3) your fixed-price bid. One paragraph max. Generic bids ignored.