Techempower AI Developer Bootcamp: Learn AI, Get Hired

Build Real AI Agents. Get Hired.

Are you a Python developer looking to learn to build RAG / LLM-based applications? Our AI Developer Bootcamp is a hands-on program where you’ll build your own portfolio-ready LLM application from scratch.

You’ll learn the same skills that we use every day on real-world projects, helping you stand out in today’s tough job market. No prior LLM experience needed!

The Bootcamp is For:

Recent grads seeking a portfolio project that helps open doors to the job pipeline
Python developers ready to add AI skills to their toolkit
Technical professionals who want hands-on LLM experience
Career changers looking to break into AI roles

Schedule & Pricing

Next Cohort Starts

Oct 20

Seats fill quickly! Apply soon.

Commitment

Part-time over approximately 6 weeks

Flexible pacing slots into your schedule, whether it’s evenings or weekends.

Pricing

$4,000 per seat

Team discounts & scholarships available for qualified recent grads

“I walked into the bootcamp without knowing anything about LLMs. Six weeks later, I’m confident I can design, build, and ship AI-powered apps—and that portfolio helped me land my new role.”

Hy Huynh · Bootcamp Graduate

How It Works

Over approximately 6 weeks, you’ll build projects step by step with direct support from industry engineers. Submit work as GitHub pull requests, receive professional feedback, and collaborate with instructors and peers through Slack and live office hours.

Why Our Bootcamp?

Unlike other courses, this program goes beyond videos and toy projects. You’ll get hands-on experience with LLMs, career materials to stand out in interviews, and graduate with a project, a story, and the confidence to put AI into practice—building a clear pipeline from learning to landing your next role..

What You’ll Do

Build your own AI agent: You’ll start with a simple chatbot and build it into a fully featured AI system – yours to keep and showcase on GitHub.
Learn industry-ready skills: Master LLM fundamentals, prompt engineering, retrieval-augmented generation (RAG), guardrails, agentic AI, and tool integration.
Work like a professional: Use GitHub pull requests, automated tests, code reviews, and TODO-driven scaffolding to practice professional SDLC workflows.

What You’ll Get

Portfolio project: a working agent repo you can discuss in interviews.
Mock interview + feedback and a résumé/LinkedIn tune-up.
Completion certification and LinkedIn announcement to 15k followers.

Syllabus

LDB-001 · LLM Fundamentals in Python

Kickstart your repo and ship a minimal tech-support assistant using pure Python and the OpenAI SDK (no third-party libs). Understand tokenization, context windows, and how cost/latency affect quality.

You’ll build: a CLI/web script that answers support questions and preserves short conversational context.

LDB-002 · Prompt Engineering

Design prompts that clarify, summarize, and adapt to user preferences. Add lightweight telemetry to track token usage and spot regressions.

You’ll build: reusable system/user prompt templates + a small “prompt budget” dashboard.

LDB-003 · Retrieval-Augmented Generation (RAG)

Ground answers in your own documents and fail gracefully with fallbacks and “I don’t know (but here’s what to try)” patterns.

You’ll build: a simple RAG pipeline (ingest → chunk → embed → retrieve) and an eval script to compare answer quality.

LDB-004 · Guardrails for LLM Apps

Align behavior with business rules, safety, and compliance. Filter risky queries, enforce tone/policy, and log moderation events.

You’ll build: a guardrail layer (policy checks + moderation hooks) that runs before/after model calls.

LDB-005 · From Assistant to Agent

Go beyond chat: add actions and escalation. Trigger webhooks, post to Slack, and hand off to humans when confidence is low—prioritizing critical cases.

You’ll build: Slack alerts + webhook actions with a scoring system that routes issues faster.

LDB-006 · Tool Use with Reasoning Loops & MCP

Implement plan-act-observe loops so your assistant can call tools, analyze results, and iterate toward a solution. Use Model Context Protocol (MCP) to register tools via a common interface.

You’ll build: a reasoning loop that chains multiple tool calls and an MCP tool registry your agent can query at runtime.

By the end, you’ll have

A working agent repo (prompts, RAG, guardrails, tools, evals).
Ops basics: cost/latency tracking, safety logs, and smoke tests.
Integrations: Slack + webhook-based actions you can extend.
A shareable demo and README for your portfolio.

Ready to Get Started?

Join the next cohort and start building your AI portfolio project today.

About TechEmpower

TechEmpower is a software consulting and development firm in Los Angeles. For more than 25 years, we’ve built platforms and products for startups, nonprofits, and enterprises. Today, we focus on bringing AI into real-world development, helping developers build the skills to innovate with impact.