Why AI Engineering Now
The gap is real. The window is open.
In the past two years, AI has created 1.3 million new roles globally — and only 3% of the workforce has the skills to fill them. LinkedIn ranks AI Engineer among the fastest-growing jobs of 2026. The demand is there. The talent isn't. Yet.
AI Native Full Stack — Program Modules
Build real AI-powered systems from day one. This curriculum is organized around two tracks — Core AI and Agents, and Infrastructure for AI — plus a capstone that integrates everything into a fully transformed, deployed company. Supported by complementary foundations in modern development tools and practices throughout.
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.
The Most Personalized Path to an AI Career
From AI-powered feedback to unlimited 1:1 mentorship and lifelong career support — every part of the experience is built around you, not a classroom of thirty.
AI-powered feedback that never sleeps
Unlimited 1:1 mentorship, for life
Career support built for the AI job market
AI-powered feedback that never sleeps
- Instant feedback from Rigobot, our custom AI tutor
- Advice adjusted to your skill level and progress
- 24/7 help with debugging and problem-solving
- Hundreds of interactive coding exercises and tests
Career Outcomes
Graduate with the credentials, portfolio, and professional profile to get hired — not just qualified.
Certificate
of Achievement
This certificate is presented to
Pedro Fuentes Escaloso de los Lobos
Ready to start your tech career?