Become a Certified AI Engineer in Europe in Just 9 Months
Built to get you job-ready.
Live cohorts, real labs, career support to placement. Once these seats are gone, the next intake is months away.
🔥 Why Should You Study Artificial Intelligence and Start Your AI Career With Us?
💰 $100,000+ Salary Potential — AI professionals can reach six-figure salaries in many countries, depending on their skills, experience, and role.
🌍 Work From Anywhere — Work with international companies, find remote or freelance opportunities, or become your own boss by building your own AI business.
📈 Millions of Future Opportunities — AI adoption is growing across industries, creating strong demand for professionals who can build and apply AI solutions.
💻 6 Months of Career Support After Graduation — Our Career Services team supports you after completing the program and helps guide you through your career journey.
💼 Gain Real AI Experience in 9 Months — Learn through practical projects, build your portfolio, and develop skills you can showcase to employers.
🏆 Become a Certified AI Engineer — Earn a professional EURECIN certification and strengthen your CV with a qualification focused on the future of technology.
EURECIN Institute vs. Other Schools
EURECIN Institute
✅ Certified AI Engineer Cerificate + 9 Months of Practical Project Experience
✅ Add practical experience to your CV
✅ Learn while gaining experience
✅ Graduate with projects + experience + certificate
Other Schools
❌ Theory-Based Learning — No Real Project Experience
❌ Complete theoretical assignments
❌ Experience comes after graduation
❌ No real project experience
9 Months of Learning = 9 Months of Experience
At EURECIN, we focus on practice from Day One.
You don’t spend months only studying theory before gaining practical experience. Throughout the program, you learn the concepts and immediately apply them through practical exercises and real project work.
Learn → Practice → Build → Gain Experience
- 🧠 Learn the essential theory
- 💻 Practice from Day One
- 🛠️ Build projects throughout the program
- 📈 Develop practical AI Engineering skills
- 📁 Build your professional portfolio
- 🚀 Gain 9 months of practical project experience during your 9-month program
Not 20% Theory. 80% Practice.
By the time you graduate, you haven’t just completed a 9-month course. You have spent 9 months learning, practicing and building AI projects.
62 +
Nationalities Represented
1,000 +
Graduates Placed in Europe
87 %
Employment Rate
15+
European Countries
Our graduates are part of leading companies like
What Our Certified Graduates Say
An investment in your next career.
Become a Certified AI Engineer
Build practical AI skills and strengthen your professional profile with a structured online program designed for aspiring AI professionals and career changers. Upon successful completion, you will receive a European-issued Certified AI Engineer certificate from EURECIN. The program combines practical learning, real-world AI applications, and professional development to help you gain confidence, demonstrate your skills, and prepare for AI career opportunities in Europe and international markets.
Who Can Join the EURECIN Certified AI Engineer Program?
To join the program, you should meet the following requirements:
✅ 18+ Years Old — The program is open to adults who want to develop a career in Artificial Intelligence.
✅ Basic English Level — You should understand basic English to follow the course materials, technical concepts, and assignments.
✅ Laptop or Computer Required — A personal laptop or computer is required to complete practical exercises, work on AI projects, and access the online learning platform.
✅ Educational Background — A high school diploma or equivalent is recommended but is not mandatory.
✅ Motivation to Learn — A strong interest in technology and commitment to completing the program, practical projects, and assessments.
What You Will Learn?
Follow a complete AI Engineering learning journey designed to help you develop the technical knowledge, practical skills, and professional experience needed to become a Certified AI Engineer. Through structured modules, practical exercises, and real-world projects, you will build the skills needed to work with modern artificial intelligence technologies.
Module 1 — AI & Computer Science Foundations
- Introduction to Artificial Intelligence
- AI, Machine Learning and Deep Learning
- Types of Artificial Intelligence
- AI Applications and Use Cases
- Algorithms and Computational Thinking
- Data Structures
- Programming Fundamentals
- Development Environments
- Git and GitHub
- Linux and Command Line
- Software Development Fundamentals
Practical Project: Develop a small software application that applies fundamental programming and computational-thinking concepts.
Module 2 — Mathematics for AI
- Mathematical Foundations for AI
- Algebra and Functions
- Vectors and Vector Operations
- Matrices and Matrix Operations
- Linear Algebra
- Probability Fundamentals
- Statistics for AI
- Distributions
- Correlation and Covariance
- Calculus Fundamentals
- Derivatives and Gradients
- Optimization
- Mathematical Foundations of Machine Learning
Practical Project: Apply mathematical and statistical methods to analyze a real-world dataset and interpret the results.
Module 3 — Python for AI
- Python Fundamentals
- Variables and Data Types
- Operators and Expressions
- Conditional Statements
- Loops
- Functions
- Lists, Tuples, Sets and Dictionaries
- File Handling
- Error Handling and Exceptions
- Object-Oriented Programming
- Modules and Packages
- Virtual Environments
- NumPy
- Pandas
- Matplotlib
- Data Visualization
- Working with APIs
- Testing and Debugging
Practical Project: Develop a Python application that processes data, performs calculations and generates useful results.
Module 4 — Data Engineering for AI
- Introduction to Data Engineering
- Data Collection
- Data Sources and Formats
- Data Cleaning
- Missing Data
- Data Transformation
- Data Preprocessing
- Exploratory Data Analysis
- SQL Fundamentals
- Relational Databases
- Database Queries
- Data Integration
- Data Pipelines
- Feature Engineering
- Data Quality
- Data Visualization
- Preparing Data for Machine Learning
Practical Project: Build a complete data-processing workflow that collects, cleans, transforms and prepares data for an AI application.
Module 5 — Machine Learning
- Introduction to Machine Learning
- Supervised Learning
- Unsupervised Learning
- Regression
- Linear Regression
- Logistic Regression
- Classification
- K-Nearest Neighbors
- Decision Trees
- Random Forests
- Support Vector Machines
- Clustering
- K-Means
- Dimensionality Reduction
- Feature Selection
- Feature Engineering
- Training and Testing
- Cross-Validation
- Model Evaluation
- Hyperparameter Tuning
- Model Selection
- Machine Learning Pipelines
Practical Project: Develop and evaluate a machine learning solution that makes predictions or classifications from a real-world dataset.
Module 6 — Deep Learning
- Introduction to Deep Learning
- Neural Networks
- Perceptrons
- Layers and Architecture
- Activation Functions
- Loss Functions
- Gradient Descent
- Backpropagation
- Optimization Algorithms
- Regularization
- Overfitting and Underfitting
- Training Neural Networks
- PyTorch
- Convolutional Neural Networks
- Recurrent Neural Networks
- Sequence Models
- Attention Mechanisms
- Transformers
- Model Evaluation
Practical Project: Build, train and evaluate a deep learning model to solve a real-world prediction or classification problem.
Module 7 — Computer Vision
- Introduction to Computer Vision
- Digital Images and Image Representation
- Image Processing
- OpenCV
- Image Classification
- Convolutional Neural Networks
- Transfer Learning
- Object Detection
- Image Segmentation
- Feature Extraction
- Data Augmentation
- Vision Transformers
- Video Analysis
- Multimodal Vision
- Computer Vision Model Evaluation
Practical Project: Develop a computer vision application capable of analyzing and interpreting visual data.
Module 8 — Natural Language Processing
- Introduction to NLP
- Text Processing
- Text Cleaning
- Tokenization
- Stop Words and Stemming
- Text Representation
- Word Embeddings
- Text Classification
- Sentiment Analysis
- Named Entity Recognition
- Sequence Models
- Attention Mechanisms
- Transformers
- BERT and Similar Models
- Transfer Learning for NLP
- NLP Model Evaluation
Practical Project: Develop an NLP application that analyzes and extracts meaningful information from real-world text data.
Module 9 — Generative AI & Large Language Models
- Introduction to Generative AI
- Generative AI Applications
- Large Language Models
- How LLMs Work
- Tokens and Tokenization
- Transformer Architecture
- Attention Mechanisms
- Prompt Engineering
- System Instructions
- Few-Shot and Zero-Shot Learning
- Structured Outputs
- Function Calling
- Working with LLM APIs
- Open-Source LLMs
- Model Selection
- LLM Evaluation
- AI Application Development
Practical Project: Build an AI-powered application using a large language model and an external API.
Module 10 — Retrieval-Augmented Generation
- Introduction to RAG
- RAG Architecture
- Embeddings
- Vector Representations
- Vector Databases
- Document Processing
- Document Chunking
- Information Retrieval
- Similarity Search
- Metadata Filtering
- Hybrid Search
- Reranking
- Retrieval Pipelines
- Context Construction
- RAG Evaluation
- Improving RAG Performance
Practical Project: Develop an AI application that retrieves information from a private collection of documents and generates context-aware responses.
Module 11 — AI Agents
- Introduction to AI Agents
- Agent Architecture
- LLMs as Agent Reasoning Engines
- Tools and Tool Calling
- Actions and Observations
- Agent Workflows
- Planning
- Memory
- Multi-Step Reasoning
- Agentic RAG
- Multi-Agent Systems
- Agent Frameworks
- LangGraph
- LlamaIndex
- Agent Evaluation
- Human-in-the-Loop Systems
- Model Context Protocol
Practical Project: Develop an AI agent capable of using multiple tools to complete a multi-step task.
Module 12 — Fine-Tuning & Advanced AI
- Introduction to Model Fine-Tuning
- Pre-Trained Models
- Transfer Learning
- Instruction Tuning
- Dataset Preparation
- Fine-Tuning Workflows
- Parameter-Efficient Fine-Tuning
- LoRA
- QLoRA
- Quantization
- Model Compression
- Local AI Models
- Inference Optimization
- Model Evaluation
- Fine-Tuning Evaluation
- Advanced LLM Applications
Practical Project: Adapt an existing AI model to a specific task using a custom dataset and evaluate its performance.
Module 13 — AI Engineering & Deployment
- Introduction to AI Engineering
- AI Application Architecture
- REST APIs
- FastAPI
- Backend Development
- Model Serving
- Application Integration
- Authentication
- Databases
- Docker
- Containerization
- Cloud Computing
- AI Application Deployment
- CI/CD
- Application Testing
- Logging
- Monitoring
- Scaling AI Applications
Practical Project: Develop and deploy a complete AI application with an API, backend and production-ready deployment environment.
Module 14 — MLOps & LLMOps
- Introduction to MLOps
- ML Lifecycle Management
- Experiment Tracking
- Model Versioning
- Data Versioning
- Model Registry
- ML Pipelines
- Automated Training
- Continuous Integration
- Continuous Deployment
- Model Deployment
- Model Monitoring
- Model Drift
- Model Performance Monitoring
- LLM Evaluation
- LLM Monitoring
- Cost Optimization
- AI Infrastructure
- Production AI Operations
Practical Project: Create an end-to-end workflow for deploying, monitoring and maintaining an AI model or LLM application.
Module 15 — AI Ethics & Security
- Introduction to Responsible AI
- AI Ethics
- Bias and Fairness
- Transparency
- Explainability
- Privacy
- Data Protection
- Responsible AI Development
- AI Governance
- AI Security
- Prompt Injection
- Data Poisoning
- Adversarial Attacks
- Model Security
- AI Application Security
- Secure AI Development
- AI Regulation and Compliance
Practical Project: Analyze an AI system for ethical, privacy and security risks and develop measures to improve its responsible and secure operation.
Module 16 — AI System Design
- Introduction to AI System Design
- AI System Architecture
- Requirements Analysis
- Model Selection
- Data Architecture
- Database Selection
- Vector Database Architecture
- API Architecture
- AI Application Architecture
- Scalability
- Performance
- Latency
- Reliability
- Fault Tolerance
- Security Architecture
- Cost Optimization
- Monitoring and Observability
- Production AI Systems
- System Design Trade-Offs
Practical Project: Design a complete production-ready AI system, including its architecture, models, data flow, infrastructure, security, scalability and deployment strategy.
Module 17: AI Projects, Portfolio & Career Preparation
- Develop a complete AI project.
- Build a professional AI portfolio.
- Prepare your CV and LinkedIn profile for AI roles.
- Learn how to present your AI skills to employers.
Final Project:
🎓 Create and present a practical AI project demonstrating your knowledge and skills as a Certified AI Engineer.
Our Certificate
The EURECIN AI Engineer Certificate enhances the credibility of your professional profile and helps you stand out to employers across Europe and internationally through a respected European-issued certificate. Combined with practical projects, hands-on experience, and dedicated career and job support, the program equips you with the knowledge, practical skills, confidence, and guidance needed to pursue AI career opportunities in Europe and around the world.
Next Program Start: 20 September 2026
Certified AI Engineer Program
In 9 Months
🚀 Develop future-ready AI skills and prepare for opportunities in one of the world’s fastest-growing technology industries.
Earn a professional EURECIN AI qualification designed to strengthen your technical profile and career opportunities.
Build 9 months of real AI project experience and a professional portfolio to showcase your skills to employers.
Unlock remote and international career opportunities with practical AI knowledge valued by companies worldwide.
Land a tech job in 3 to 6 months (depending on your profile, skills, and commitment). Receive 6 months of career support from our Career Services team to guide you throughout your journey.
Flexible payment options are available through bank transfer. You can choose to pay in full with a one-time payment or split the tuition into 2 or 3 payments. Payment is completed exclusively by bank transfer.
€2,900 - One-Time Payment
€280 Discount Applied Pay the full amount upon enrollment.
€3,000 - 2-Payment Plan
€100 more than paying in full. €1,500 upon enrollment, €1,500 after 90 days (Month 4).
€3,180 - 3-Payment Plan
€280 more than paying in full. €1,060 upon enrollment, €1,060 after 60 days (Month 3), €1,060 after 120 days (Month 5).
Upcoming Program Start Dates
From Application to Program Start
1. Read the program details and make sure you meet the enrollment requirements.
2. Complete the online application form. After submitting it, the payment instructions will be displayed on your screen.
3. Choose your preferred payment option (one-time payment or installments in 2 or 3 payments) and send your proof of payment to our email address.
4. Once your payment has been confirmed, your enrollment will be finalized, and you will officially begin the program.
For bank transfer, the account details will be displayed after submitting the form. Please send your proof of payment to contact@eurecin.eu. Confirmation of your enrollment will follow by email.
Join a global community
Yes. Our goal is not only to help you gain a professional AI qualification but also to support you in increasing your chances of finding employment opportunities in the AI industry.
After completing the EURECIN Certified AI Engineer Program, you will receive up to 6 months of dedicated career and job support, including:
✅ Career Coaching — Guidance to help you understand your career options and prepare for the AI job market.
✅ Proven Job Search Strategies — Learn effective methods to identify and approach AI career opportunities.
✅ Practical Application Guidance — Support with applications, interview preparation, and improving your job search approach.
✅ CV, LinkedIn & Portfolio Support — Help presenting your AI skills and projects professionally to employers.
Career Opportunities
Depending on your skills, experience, and portfolio, you can explore opportunities such as:
🤖 AI Engineer
🧠 Machine Learning Engineer
💻 AI Developer
⚙️ AI Automation Specialist
📊 AI Data Analyst
💬 Prompt Engineer
🌍 AI Consultant
🚀 AI Product Specialist
Choose Your Career Path
🌍 Work With International Companies — Pursue opportunities with companies around the world, including remote positions.
💻 Remote or Freelancing — Offer your AI skills and services to clients internationally.
🚀 Become Your Own Boss — Use your AI skills to create your own services, products, automation solutions, or AI business.
Our team supports you throughout your career journey, and in some cases, support may continue beyond 6 months depending on your progress and engagement.
Yes. The program is fully online and can be completed from anywhere in the world.
You can also join while working full time, as the training is flexible and designed to fit around your schedule. Most candidates study part-time alongside their job and progress at their own pace.