Become an AI Engineer. The Fastest-Growing Job of 2026.
Own the skills companies are competing to hire for. In 22 weeks, you'll transform a real company into a complete AI-native business, building AI agents, automations, and real-time systems. With unlimited mentorship and career support for life.
- Direct AI coding agents with precision, so you can truly trust their output.
- Orchestrate multi-agent systems that run complex processes end to end, the way top-tier engineering teams do.
- Graduate with an industry-recognized AI Engineering certificate.
- Land roles paying $95K–$135K+. 84% of graduates get hired within 3–6 months.
Your tutors:
Marco Gonzalo
Main Instructor
Rigo
AI Tutor
Your AI Engineering Career Starts Here.
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Real Outcomes. Real Numbers.
8,500+ graduates. Strong outcomes across the board.
84%
Average hiring rate
55%
Average salary increase at new position
3-6 months
Average time to get hired
The demand is there. The talent isn't.
Why AI Engineering Now
AI Engineer is the #1 fastest-growing job in the U.S., two years running, and AI-related roles have grown by 1.3 million globally in the same two years. Meanwhile, 72% of employers say they can't find the AI talent they need. The demand is there. The talent isn't. That's your opportunity.
More Jobs Created Than Lost to AI
Job creation vs. displacement · WEF Future of Jobs 2025 · by 2030
net new jobs created by AI-driven transformation, globally
Employers Can't Find AI Talent
39,000 employers, 41 countries · ManpowerGroup 2026
of employers globally report difficulty hiring for AI-related roles
of employers globally report difficulty hiring for AI-related roles
Demand Is Accelerating
AI-related roles created globally · LinkedIn Economic Graph · Jan 2026
new AI roles created globally in two years
AI Engineering Syllabus
You learn by building, not memorizing. In 22 weeks, you'll complete 55+ hands-on projects, working through two tracks: Core AI and Agents, and Infrastructure for AI, closing with a capstone that integrates everything into a fully transformed, deployed AI-native company. One continuous build, start to finish.
Core AI and Agents
Personal Assistants with OpenClaw
- Configure an open-source AI agent as a personal assistant
- Assign tasks and integrate external applications with the agent
Projects:
Deploy and configure a self-hosted AI assistant — yours, under your control, without relying on external vendors.
Core AI and Agents
Advanced Personal Assistants with OpenClaw
- Identify scenarios where an agent solves real business problems
- Develop custom skills for OpenClaw
- Implement contextual memory (episodic, semantic, procedural)
- Advanced configuration and extension of the agent
Projects:
Take your basic assistant agent to a productive tool with real autonomy in business contexts.
Core AI and Agents
Working with AI Coding Agents
- Build memory banks and context rules from an existing codebase
- Write executable specifications for agents (agent specs)
- Synthesize reusable skills so agents act with precision
Projects:
Build memory banks and context rules that turn a coding agent into a collaborator that understands your codebase.
Core AI and Agents
LLMs, Training & RAG
- Prepare data and select models for training
- Implement RAG techniques on proprietary knowledge bases
- Work with vector databases
- Evaluate, debug, and integrate models in production
Projects:
Implement RAG so your agent answers with proprietary, up-to-date knowledge.
Core AI and Agents
Agentic Engineering
- Build agents with tool calling (real function calls)
- Implement guardrails as a security and control mechanism
- Provide tools to the agent via CLIs optimized for AI
- Extend agent capabilities with the Model Context Protocol (MCP)
Projects:
Build agents that call tools, access external systems via MCPs and CLIs, and operate with persistent memory.
Core AI and Agents
Agentic Workflows
- Design multi-agent systems with routing and arbitration
- Implement shared memory across agents
- Deploy agentic workflows with serverless and durable functions
Projects:
Design systems where multiple agents collaborate, distribute tasks, and run autonomously at scale.
Infrastructure for AI
Backend Development with Coding Agents
- Design backend architectures for AI-powered solutions
- Create agent loops integrating LLMs with APIs
- Implement lightweight storage and CSV data processing
- Build and expose REST APIs for frontends and agents
Projects:
Build robust APIs with FastAPI, implement agent loops in Python, and design backend architectures for AI use cases.
Infrastructure for AI
Workflow Automations
- Model business logic with workflow diagrams
- Implement basic and advanced flows in n8n
- Integrate LLMs and external apps in automations
- Deploy maintainable workflows with error handling
Projects:
Build AI-powered business automations in n8n that run autonomously without manual intervention.
Infrastructure for AI
Data Pipelines
- Manipulate and prepare datasets with Python
- Build data pipelines from the application to analysis systems
Projects:
Build pipelines that take raw data, transform it, and leave it ready to feed models, reports, or agents.
Infrastructure for AI
Telemetry
- Optimize storage for reporting and data integrity
- Identify data collection opportunities in real scenarios
- Collect telemetry and user context from the application
- Build reports from telemetry data
Projects:
Instrument applications to collect behavioral data and make decisions based on real evidence.
Infrastructure for AI
Asynchronous Processing and Offloading
- Implement background processing for costly tasks
- Manage process queues with workers
- Use queues to delegate work between agents and services
Projects:
Implement background processing and queue systems that let agents delegate heavy work without blocking users.
Infrastructure for AI
Real-Time
- Build support chats with LLMs in real time
- Implement response streaming with generators (yield)
- Integrate webhooks and pub/sub in AI applications
Projects:
Implement real-time communication between users and language models using streaming, WebSockets, and event-driven architectures.
Infrastructure for AI
Web Application Authentication
- Implement authentication and route restrictions in FastAPI
- Build complete authentication flows (login, tokens, sessions)
Projects:
Implement secure authentication in FastAPI and build complete login flows that define what each user — and agent — can do.
Infrastructure for AI
Error Handling, Debugging and Testing
- Understand and manage runtime errors with flow control
- Develop test suites for robust applications
Projects:
Verify AI-generated code with controlled error handling and test suites that validate expected behavior.
Infrastructure for AI
Cybersecurity in AI Applications
- Identify and fix OWASP Top 10 vulnerabilities in web applications
- Implement security practices specific to AI integrations
- Use LLMs as a cybersecurity auditing tool
Projects:
Identify critical vulnerabilities in AI applications and implement safe practices in model integration.
Capstone
AI-Transformed Company
- AI-generated frontend
- API with full authentication
- Telemetry and reporting pipeline
- Agent-generated automated workflows
- RAG knowledge layer
- Agents with tool calling
- Real-time communication
Projects:
Integrate everything you have learned into a working, deployed system — a full transformation of a company through AI.
22 Weeks From Now, You Could Be an AI Engineer.
The market's moving fast. This is your opportunity.
Apply Now
22 Weeks From Now, You Could Be an AI Engineer.
The market's moving fast. This is your opportunity.
Apply Now22 Weeks From Now, You Could Be an AI Engineer.
The market's moving fast. This is your opportunity.
Apply NowSupport That Keeps You Moving. And Gets You Hired.
22 weeks is intense, and it's crucial not to lose momentum halfway through. That's why support is built into every step: AI feedback 24/7, 1:1 mentorship sessions focused entirely on you, and career support to land your first role.
AI-powered feedback 24/7
Unlimited 1:1 mentorship, for life
Career support built for the AI job market
AI-powered feedback 24/7
- Never get stuck. Rigobot gives instant feedback any hour, day or night.
- Get advice that actually fits you, adjusted to your skill level and progress.
- Build real skill, not just theory, with hundreds of hands-on coding exercises.
- Move forward with confidence, no more guessing if you're doing it right.
The Roles You Can Land. And the Salaries That Follow.
LinkedIn reports 42x growth in Forward-Deployed Engineering alone since 2023.
$105K–$155K: Forward-Deployed Engineer
$95K–$135K: AI Software Engineer
$90K–$130K: AI-Fluent Full Stack Engineer
Top rated across all major platforms.
Where Our Graduates Work
From startups to global tech leaders, our graduates work at some of the most recognized companies worldwide.
Transforming ideas into digital experiences.
Building the future of work together.
Scaling solutions for modern businesses.
Transforming ideas into digital experiences.
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