22 Weeks To a $95K–$135K AI Engineering Career.
- The return: a $95K–$135K entry-level salary, with demand up 13x since 2023.
- 22 weeks to job-ready, not years: a shipped AI-native portfolio and the skills companies are racing to hire.
- Job-or-Refund option: get hired within 9 months, or your full tuition back. The risk is on us, not you*.
Your Roadmap to a High-Paying Career
Tuition, financing options, syllabus and salary data, sent straight to your inbox.
Get a High-Paying Position. Fast.
6,000+ graduates worldwide. Now thriving in tech.
84%
Average hiring rate
55%
Average salary increase at new position
3-6 months
Average time to get hired
Everything You Need to Launch Your AI Engineering Career
The portfolio, skills, and support behind the outcomes
Real projects, current tools, expert guidance and 1:1 support whenever you need it. This is what a program built to launch a $95K–$135K career looks like.
Trained on What's Hiring Right Now
LangChain, RAG, PyTorch, vector databases, deployment. We rebuild the syllabus around real AI Engineer job postings, not outdated theory.
Career Support That Gets You Hired
Support built for the AI job market, not a generic playbook: career coaching and a network that doesn't disappear at graduation.
Keep Moving with Unlimited Access to Expert Mentors
Get unstuck fast. Book a 1:1 mentorship session whenever you need it, no caps, no long waits. Available for life.
Graduate With Proof, Not Promises
A complete AI-native business built by you, not disconnected exercises: real RAG systems, agents, and working AI APIs, shipped start to finish. The kind of full-system portfolio companies are actually hiring for.
How We Get You Hired. From First Day to Signed Offer.
A complete system that takes you from enrolled to employed. AI-optimized profiles, direct access to 400+ hiring partners, and 1:1 support that's with you beyond landing your first role.
Build a Recruiter-Ready Profile, Smarter.
We'll help you create tailored job search materials
Career Strategy, Built Around You
Ready to start your AI career?
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
Program Overview
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.
See the Real ROI
What you pay. What you get. See how fast it pays off
The Investment
$15,999 TuitionFull tuition is $15,999. Pay upfront and save 10%, split it into 6 interest-free monthly payments at no extra cost, or spread it out further with financing through our lending partners starting at $349/mo over 24 to 60 months with interest.
The Return
$95K–$135K Entry-Level SalaryAI Engineer is the #1 fastest-growing job in the US, with demand up 13x since 2023. On a $95,000 starting salary, tuition pays back in about 2 months once you're hired, and our graduates land a position in an average of 3–6 months.
What Happens After You Hit Submit
Three steps. No surprises.
Syllabus, pricing and additional program information land in your inbox immediately.
An advisor calls within 24 hours to walk you through the details and answer your questions.
You decide, on your own time. No pressure.
Syllabus, pricing and additional program information land in your inbox immediately.
An advisor calls within 24 hours to walk you through the details and answer your questions.
You decide, on your own time. No pressure.
Real Careers Start Here — Just Ask Our Students
Our grads are in high demand, with over 84% hired within six months of finishing the program.
Loretta Thompson
United Way Miami
Loretta joined in 2022 and graduated in 2023. She already found a job within the next few months and has fulfilled the whole circle of skills+job that we all want to complete!
Rich Akers
Clark University
Richard is a great developer that just transitioned from a different background and is now working as a web dev in the tech field.
MaiLinh Tran
Clark University
An entrepreneur with a passion for technology is now focusing on the dev side of their endeavour.
Jorge Martín Coimbra
UTEC-BID
Martín joined the first program that we launched together with UTEC and IDB. He got a better paying job, a career that he is passionate about and a new professional life.
Jean St. Cloud
Clark
Jean is transitioning from other industries (Music) and is finding his way into Tech. He got it with the support of Clark University and is already performing as a developer and a Mentor.
Alexandra Espinoza
CINDE-BID
Alexandra came from a total different background is been a huge revelation for her and everyone around her. She is now a successful and talented software developer in Costa Rica.
Gabriel Salazar
CINDE-BID
Gabriel was already a support specialist at Microsoft and after completing the program he was able to achieve a new position within Microsoft where he is now working as a Software Engineer.
Laura Magallanes
UTEC-BID
Laura is just extraordinary. From a little town in Uruguay with no experience in Coding, she is now a woman head of household, an Instructor and a Program Coordinator.
Melanie Galaretto
UTEC-BID
Melanie is a young professional who is dreaming of achieving a life that now she owns. She is a resourceful and committed software developer working for a software firm in her home country.
Natia Lombardo
UTEC-BID
Natia is a philosopher and a software developer. Currently working as a QA Engineer of a huge and successful International firm.
Luis Larraburo
CINDE-BID
Luis came to the program without any previous experience, He is now a software developer working on a tech firm in Costa Rica.
Leandro Matonte
UTEC-BID
Leandro got into the program with the expectation to achieve a better understanding and some coding skills that will help with his decision of being a computer scientist graduate. Now he is a software developer at a tech firm in Uruguay.