AI Engineer
What You’ll Master in This Bootcamp
The AI Engineer curriculum is structured into four focused modules.
- AI Agentic Development
- Claude Code — Introduction, installation, and setup for agentic coding in the terminal and IDE, plus an ideal agentic project directory structure.
- Spec-Driven Development — Use structured specs to get production-ready code on the first pass (Spec Kit, Superpowers, etc.).
- Model Context Protocol (MCP) — Connect to Jira, Playwright, and Chrome DevTools MCPs to go from ticket → code → tested feature end-to-end.
- Agent Skills — Build AI Skills and package reusable capabilities as SKILL.md files to automatically apply the right expertise to the right task.
- Agentic Teams — Delegate long-horizon tasks to specialized sub-agents (Architect, Developer, Code Reviewer, QA Tester) running in isolated context windows and orchestrated under a main agent.
- Agentic Coding — Ship features with CLAUDE.md, DESIGN.md, AGENTS.md, hooks, slash commands, and rules.
- Hands-On Bootcamp Projects — Build multiple end-to-end agentic applications with AI-driven quality testing from scratch, applying Claude Code, MCP, Skills, and sub-agents to real-world use cases you can add to your portfolio.
- Prompt Engineer with Python
- Python for AI & Data Handling — Learn Python from the ground up, including data processing, APIs, error handling, and AI model QA automation.
- Prompt Engineering & In-Context Learning — Master zero-shot (ZSL), few-shot (FSL), chain-of-thought (CoT), self-consistency, and tree-of-thought (ToT) prompting.
- Structured Outputs & Data Extraction — Extract structured data from unstructured text (PDFs, documents) and validate it for production pipelines.
- Prompt Optimization & Testing — Evaluate prompts systematically with golden datasets and automated grading—moving from guesswork to measurable quality.
- Generative AI Engineer
- Chaining, LangChain & Retrieval-Augmented Generation (RAG) — Build intelligent chatbots, memory-based assistants, and AI-powered search.
- AI Agents & Tool Integration (ReAct) — Combine reasoning with action to plan and execute tasks via calculators, APIs, and custom tools.
- AI Guardrails: Toxicity & Factuality — Implement safety layers with toxicity filtering, factuality scoring, and robust fallback mechanisms.
- AI QA Testing & Monitoring — Add test coverage for data pipelines, model performance, and LLM outputs to ensure production reliability.
- Model Context Protocol (MCP) — Build MCP servers and clients in Python to connect Claude to apps, databases, and internal APIs.
- Agent Evaluation & Observability — Design evals, tracing, and cost tracking to measure agent reliability in production.
- AI Fluency & Responsible AI — Apply 4D framework (Delegation, Description, Discernment, Diligence) alongside professional and ethical best practices.
- Cloud Deployment (AWS SageMaker & Bedrock) — Take models live with scalable, enterprise-grade inference.
- AI Researcher
- Deep Learning & Model Training (PyTorch) — Train, specialize, and optimize foundation models using Hugging Face Transformers.
- Parameter-Efficient Fine-Tuning (PEFT) with LoRA — Fine-tune large foundation models using Low-Rank Adaptation (LoRA) and adapter methods.
Address
830 Stewart drive, #106,
Sunnyvale, CA 94085
Sunnyvale, CA 94085
