The Claude Code Setup That Staff Engineers Use
Join Marcus Chen (Principal Platform Engineer · Databricks) for a free live session.
Marcus Chen
Principal Platform Engineer · Databricks
⭐ 4.9 / 5
You have the tools. We'll teach you the system.
Most senior engineers have tried Cursor or Claude Code and ended up with larger PRs, more review cycles, and hidden technical debt. The problem isn't the tools — it's that nobody taught the system design and reasoning control behind them. This course gives you the context engineering framework, spec-driven workflow, and MCP integrations that working staff engineers use to ship production features — and teaches you how to control reasoning modes, prevent context drift, and evaluate outputs against correctness, not intuition.
What You'll Learn
AI-Native Development Workflow
CLAUDE.md and Cursor rules config that encodes your team's conventions and codebase structure — controlling how AI reasons, when to force plan-first vs direct generation, and how to tune for determinism vs exploration.
Context Engineering Framework
Context layering using .cursorrules, CLAUDE.md, and codebase indexing that ensures consistency across large codebases — minimizing context loss, preventing hallucinated dependencies, and maintaining integrity across multi-file edits.
Spec-Driven Prompting System
A reusable spec template that captures intent, constraints, and acceptance criteria before a prompt is written — turning vague feature tickets into deterministic interfaces where spec quality directly controls output quality.
Agentic Workflow System
Claude Code subagents orchestrating multi-step tasks via MCP — connected to GitHub, Jira, and your dev environment — evaluated on latency, correctness, and failure-mode analysis to determine where autonomy is safe.
Who Is This For?
This course is designed for:
Mid-Level Software Engineers
Who are coding daily but struggle with integrating AI tools effectively into their existing workflows — and want consistent, production-grade output, not hit-or-miss suggestions.
Senior Engineers Skeptical of AI
Who have tried tools like Cursor or Claude Code but experienced inconsistent output, hallucinations, or increased review overhead — and want to understand why.
Tech Leads Accountable for Team Velocity
Who need a systematic AI workflow they can adopt and roll out across their team — with clear guardrails, evaluation methods, and without introducing hidden technical debt.
Course Outline
5 weeks · 3 sessions per week
Projects You'll Ship
Leave with real work to show, not just a certificate.
Personal AI Dev Config Repo
Configured CLAUDE.md and .cursorrules files with context engineering patterns — documented so any engineer can clone it, adapt it to a new codebase, and be productive with Cursor and Claude Code from day one.
Working MCP Server Setup
Running MCP integration connecting Claude Code to GitHub (PRs, issues, commits) and at least one additional tool (Jira, Slack, or local database) — with permission model decisions and a test log demonstrable in a team engineering review.
Production Subagent Workflow
Version-controlled subagent that takes a Jira ticket and produces a draft implementation plan and PR description — end-to-end on a real feature, with failure mode analysis, evaluation metrics (correctness, latency, review overhead), and retry strategies.
Your Instructors
Marcus Chen
Principal Platform Engineer · Databricks
⭐ 4.9 / 5
Marcus Chen is the Principal Platform Engineer for AI Infrastructure at Databricks, where he runs GPU cluster operations across 2,000+ nodes on AWS and Azure and owns the LLM inference platform serving production workloads. Before Databricks, he spent five years as a Senior SRE at Google Cloud. He teaches from the infra side — not the ML side.
What Students Say
⭐⭐⭐⭐⭐
"Before this sprint, my team was averaging 3x the review comments on AI-assisted PRs. Two weeks after setting up our shared CLAUDE.md and .cursorrules, that dropped by half. The context engineering module alone justified the price."
David Park
Staff Engineer · Cloudflare
⭐⭐⭐⭐⭐
"I'd watched every Claude Code tutorial and still couldn't get consistent output in our monorepo. Week 2 on context layering fixed what 40 hours of tutorials didn't — the difference is understanding why the model loses track, not just how to prompt it."
Sarah Kim
Senior Engineer · Rippling
⭐⭐⭐⭐⭐
"The MCP GitHub integration in Week 4 changed my daily workflow more than anything I've learned in the past two years. Claude Code now triages new issues and drafts implementation plans while I'm in deep work."
Marcus Chen
Engineering Lead · Brex
Course Schedule
All sessions are instructor-led and live. Recordings available within 24 hours.
SUNDAY
9:00 AM PDT
Live ClassInstructor-led deep dive into the week's core concept with live demonstrations in a real codebase. The what, why, and how before you build it yourself.
WEDNESDAY
6:00 PM PDT
Lab SessionSmall-group implementation session. Bring your codebase, your config, your blockers. Instructor and cohort review your work in progress.
THURSDAY
6:00 PM PDT
Build & ShipComplete and commit the week's deliverable — CLAUDE.md, MCP integration, subagent workflow — with live support. Leave with a working artifact.
Frequently Asked Questions
Related reading
The JD-backed research behind this course — from Dexity Intel.
AI Engineer Career Path in 2026 →How far do you want to go?
Start free to experience our offering, choose the program length you would want to commit to.
You build. Nobody demos at you.
Every session is follow-along — you build the thing yourself while a practitioner works beside you. That is why the hours look long: they are yours to build in, with an expert on hand to guide you. None of it is a traditional lecture.
In the session, you'll: