🎉 Welcome

Welcome!

As an AI/ML Engineer, your role bridges the gap between experimentation and production.

This onboarding checklist will help you ramp up on our data stack, modeling pipeline, and product impact — from notebooks to APIs.

🛠️ Preboarding (Before Day 1)

• [ ] Offer signed and start date confirmed
• [ ] Access to: GitHub, Slack, JupyterHub / Colab / VSCode, GCP/AWS/Azure, model registry, Notion
• [ ] Shared ML architecture overview
• [ ] Credentials for data warehouse, object storage (e.g., S3), experiment tracking (MLflow/W&B)
• [ ] Added to analytics, infra, and product syncs

📅 Week 1 – Setup & Stack

🎯 Goal: Understand data, models, and infrastructure

• [ ] Set up local dev environment or cloud notebook
• [ ] Explore data sources and schemas
• [ ] Review existing models, experiments, and versions
• [ ] Walk through ML pipeline: ingest → features → train → deploy
• [ ] Join product walkthrough to understand user use cases
• [ ] Read last 3 model releases (changelogs, issues, performance)
• [ ] Run inference locally or test on staging endpoint
• [ ] Meet with Data/ML Lead and team members