Build the technical foundation to engineer AI systems.
A 12.5-month, project-driven program designed for learners who want to develop the technical skills required to build, integrate, deploy, and maintain complete AI systems.
What Is Applied AI Engineering?
The Applied AI Engineering Program is designed for learners who want to move beyond learning AI concepts and develop the ability to engineer complete AI systems.
The program covers the practical engineering lifecycle of an AI application, from writing reliable software and working with data to developing machine learning models, building deep learning and NLP systems, integrating modern language models, and deploying AI applications.
Rather than treating AI as a collection of algorithms, the program focuses on how models, data, software, APIs, applications, and deployment work together as a complete system.
Who Is This Program For?
Applied AI Engineering is designed for motivated learners who have foundational programming skills and want to master the complete AI systems engineering stack.
AI / Computer Science Students
Students who already have programming foundations and want practical AI engineering experience alongside their academic studies.
Aspiring AI Engineers
Learners who want to progress from machine learning knowledge toward complete AI application development.
ML / Data Science Learners
Learners who understand basic machine learning or data science but want stronger software engineering and deployment skills.
Software Developers
Developers who want to add machine learning, generative AI, and AI application development to their technical skill set.
Early-Career Professionals
Graduates and junior technical professionals who want to build a stronger AI engineering portfolio.
What You Will Learn
Engineering Foundations
Python engineering · Type hints · Clean code · Error handling · Linux · Git & GitHub · OOP · SQL · PostgreSQL · SQLAlchemy
Data Engineering & Visualization
NumPy · Pandas · Data cleaning · Statistical analysis · Matplotlib · Seaborn · Hypothesis testing · Correlation · Regression
Machine Learning Engineering
Scikit-learn · Pipelines · Feature engineering · Classification & Regression · Evaluation metrics · Cross-validation · Hyperparameter tuning · MLflow
Deep Learning & NLP
Neural networks · PyTorch · CNNs · Transfer learning · RNNs · LSTMs · Transformers · BERT · GPT · Hugging Face · Fine-tuning · Speech AI
AI Systems & LLM Engineering
LLM APIs · Structured outputs · Prompt engineering · Embeddings · Vector databases · RAG pipelines · Function calling · AI agents · Memory
MLOps & Deployment
FastAPI · Docker · Multi-stage builds · CI/CD · GitHub Actions · Cloud deployment · Monitoring · Logging · Prometheus · Grafana
The 12.5-Month Journey
The curriculum is progressive. Each phase builds on the previous one, moving from software and data foundations toward increasingly complete AI systems.
Build the software engineering foundation required for practical AI development.
You Will Learn
Student Record Management System
A PostgreSQL-backed application with a CLI interface, relational schema, CRUD operations, type validation, and automated CSV export.
Every Phase Ends With Something You Can Show.
Not Just Models. Complete Systems.
The program emphasizes the complete AI application lifecycle. You learn to move from a problem and data to a model, then from a model to an application, API, deployment, monitoring, and documentation.
What You Build Throughout the Program
Engineering Foundation
Professional Python, Git, SQL, PostgreSQL, and software engineering practices.
ML Portfolio
End-to-end machine learning projects with documented evaluation and reproducible workflows.
Deep Learning Experience
Practical experience with PyTorch, Transformers, NLP, and model fine-tuning.
LLM Applications
RAG systems, vector search, AI agents, tool use, and LLM-powered applications.
Deployment Experience
APIs, Docker, cloud deployment, CI/CD, monitoring, and production workflows.
Public Technical Portfolio
Projects, GitHub repositories, documentation, and deployed systems that demonstrate what you can build.
Where Can This Lead?
The program is designed to provide a foundation for several technical career paths. Your eventual role will depend on your interests, portfolio, experience, and further specialization.
AI Engineer
Build and integrate AI capabilities into complete software systems.
Machine Learning Engineer
Develop, evaluate, and deploy machine learning models and pipelines.
Applied AI Engineer
Apply modern AI techniques to practical products, services, and real-world problems.
Generative AI / LLM Engineer
Build applications using LLMs, RAG, embeddings, tools, and agents.
MLOps / ML Platform Engineer
Develop workflows and infrastructure for deploying, monitoring, and maintaining ML systems.
AI Software Developer
Combine software development with machine learning and generative AI capabilities.
The program develops a foundation that can support these career pathways. Related specializations may include NLP, computer vision, data science, AI research engineering, or AI-focused full-stack development.
How the Program Works
Live Sessions
Sessions are held every Saturday and Sunday, 1.5 hours each, covering concepts, demonstrations, and live code reviews.
Practical Assignments
Assignments are submitted through GitHub pull requests and form part of your public project portfolio.
Phase Gate Assessments
Students complete a practical gate assessment before progressing to each subsequent phase.
Engineering Mentorship
Mentorship focuses on implementation, debugging, architectural decisions, and production practices.
Small Cohorts. Closer Mentorship.
Cohorts are intentionally kept small to maintain meaningful mentor interaction, personalized code reviews, and direct technical feedback.
Program Investment
The program is divided into six phases. Students pay phase-by-phase rather than committing to the full program upfront.
| Phase | Duration | Monthly Fee | Phase Total |
|---|---|---|---|
| Phase 0 · Engineering Foundations | 2 months | PKR 6,000 | PKR 12,000 |
| Phase 1 · Data Engineering & Visualization | 2 months | PKR 6,000 | PKR 12,000 |
| Phase 2 · Machine Learning Engineering | 2.5 months | PKR 8,000 | PKR 20,000 |
| Phase 3 · Deep Learning & NLP | 2.5 months | PKR 8,000 | PKR 20,000 |
| Phase 4 · AI Systems & LLM Engineering | 2 months | PKR 10,000 | PKR 20,000 |
| Phase 5 · MLOps & Deployment | 1.5 months | PKR 10,000 | PKR 15,000 |
| Phase-by-Phase Total | PKR 99,000 | ||
- • Fees are due at the start of each month. Payment is accepted through JazzCash, Easypaisa, or direct bank transfer. Payment plans discussed during the application interview.
Policies & Requirements
Program Policies & Academic Terms
Frequently Asked Questions
Yes. Basic Python syntax and programming fundamentals are expected before entering the program.
Ready to Engineer AI Systems?
Build the technical foundation to move from experimenting with AI to engineering complete, production-grade AI systems.