IntermediateHybrid

AI Engineering

Combine artificial intelligence with product development to create innovative solutions. Learn machine learning, data analysis, and product management in an integrated program.

Students will learn how to build end-to-end AI-powered systems, from data ingestion and model selection to pipelines, APIs, deployment, monitoring, and real-world integration. By the end of this program, students will be able to: design AI system architectures, build and deploy AI models and LLM-powered applications, integrate AI into existing software products, and operate AI systems in production environments. They will graduate as AI engineers, not tool users.

8 months
Max 15 students
Starts 6/1/2026

AI Engineering

Beginner to Expert

Certification and Recognition from AltX Academy

Course Fee (Naira)₦400,000
Course Fee (USD)$0
Certification Included
Job Guarantee

Who This Course Is For

  • Developers who want to build AI-powered software
  • Backend / full-stack engineers moving into AI systems
  • Technical founders building AI-first products
  • Engineers who want production AI skills, not theory-only ML

Teaching Model

This course is: System-first, not model-first. Product-driven. Studio-integrated. Production-oriented.

  • Students learn AI only when the studio product requires it
  • Mirroring real-world engineering
  • Building production-ready AI systems
  • Using AI responsibly as a development accelerator

What You'll Learn

LLMs and modern AI systems
AI system architecture and design
Data pipelines and workflows
Machine learning fundamentals
Production deployment and MLOps
AI integration into software products
Cost optimization and monitoring
AI-assisted development workflows
Real studio product contributions

Curriculum Structure

1

AI & Systems Foundations (Beginner)

Understand what AI engineering actually is and how AI fits into software systems.

Topics:

What AI engineering is (and is not)
Types of AI systems: rule-based, ML-based, LLM-based
AI vs ML vs Data Science
When AI is the wrong solution
Overview of AI system architecture
Python refresher for AI workflows
Environment setup & tooling

Studio Application:

Analyze studio product use-cases for AI. Decide where AI adds value vs noise.

AI Integration:

Understanding AI as a component, not magic. Evaluating AI tool outputs critically.

2

Data Foundations & Pipelines (Junior AI Engineer)

Learn how data flows through AI systems.

Topics:

Data types for AI systems
Data collection & ingestion
Data cleaning & preprocessing
Feature engineering basics
Dataset versioning
Data pipelines & workflows
Data storage for AI systems

Studio Application:

Build data pipelines for studio use-cases. Prepare datasets for real AI features.

AI Integration:

Using AI to explore datasets. Validating AI-generated data insights.

3

Machine Learning Fundamentals (Intermediate)

Understand how models actually work — without becoming an academic.

Topics:

Supervised vs unsupervised learning
Common ML algorithms (high-level)
Model training workflow
Evaluation metrics
Overfitting & underfitting
Model persistence & reuse
When to use ML vs LLMs

Studio Application:

Train simple models for real features. Evaluate performance and limitations.

AI Integration:

AI-assisted model experimentation. Interpreting results correctly.

4

LLMs & Modern AI Systems (Mid-Level)

Build real LLM-powered applications.

Topics:

How large language models work (conceptual)
LLM APIs (OpenAI, Anthropic, open-source)
Embeddings & vector databases
Retrieval-Augmented Generation (RAG)
Prompt design for production systems
Token management & cost control
AI agents & workflows
Multi-step reasoning systems

Studio Application:

Build LLM-powered features for studio app. Implement RAG for real data. Handle errors, latency, and cost.

AI Integration:

Using AI to test AI (meta-evaluation). Prompt versioning & improvement.

5

AI System Architecture & Integration (Senior-Level)

Design AI systems that scale and integrate cleanly.

Topics:

AI system design patterns
Orchestration frameworks
API design for AI services
Sync vs async AI workflows
Caching & performance optimization
Security & access control
Cost optimization strategies
AI failure modes & fallbacks

Studio Application:

Integrate AI services into backend systems. Build fallback logic for failures. Optimize performance and cost.

AI Integration:

AI-assisted architecture reviews. Simulating failure scenarios.

6

Deployment, Monitoring & MLOps (Senior → Expert)

Operate AI systems in production.

Topics:

Model serving strategies
Containerization basics
Cloud deployment (AWS/GCP/Azure)
CI/CD for AI systems
Monitoring & observability
Model drift & retraining strategies
Logging, metrics, and alerts
Compliance & data privacy

Studio Application:

Deploy AI services to the cloud. Monitor live AI features. Iterate based on real usage.

AI Integration:

AI-assisted monitoring analysis. Using AI to detect anomalies.

7

Advanced AI Engineering & Real-World Mastery (Expert)

Build any AI system confidently.

Topics:

Designing AI-first products
Choosing between models & architectures
Building custom pipelines
Multi-modal AI systems (text, image, video)
Scaling AI products
Ethical AI & responsible deployment
AI system audits & reviews
Leading AI engineering teams

Studio Application:

Design and build a complete AI system end-to-end. Defend architectural decisions. Prepare system for real users or investors.

AI Integration:

Master AI engineering workflows. Balance innovation with responsible deployment.

Prerequisites

Required:

  • Basic programming knowledge (Python strongly recommended)
  • Comfort with APIs and basic software concepts
  • Willingness to think in systems, not scripts

Helpful but not required:

  • Backend development fundamentals
  • Familiarity with Git and basic cloud concepts

Final Studio Deliverables

  • Designed and built multiple AI-powered systems
  • Deployed AI services to production environments
  • Integrated AI into real software products
  • Built and operated AI pipelines and workflows
  • Demonstrated the ability to build any AI system from idea to deployment

Evaluation & Certification Criteria

Certification is earned, not automatic. Students are assessed based on:

System design quality
Correctness and robustness
Cost and performance awareness
Security and ethics
Ability to explain and defend decisions

Career Outcomes

AI Engineer
Data Scientist
Product Manager
ML Engineer

Course Pricing

₦400,000
Nigerian Naira
$0
US Dollar

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