Java-first AI engineering
Learn to build real AI applications using Spring Boot and Spring AI instead of generic Python-only tutorials.
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SOFTWARE DEVELOPMENT · ADVANCED LEVEL
AI for Java Developers
Master AI engineering with Java and Spring AI — from prompt engineering and RAG to agents, production resilience, and building neural networks and LLMs from scratch.
Advanced24 weeks180 hoursHybrid
Your selected course will be carried into Ingress Portal.
COURSE HIGHLIGHTS
From API calls to first principles
Go beyond using LLMs — understand and build the math, neural networks, and transformer architecture behind them.
Production-grade architecture
Cover resilience, security, observability, and scalability patterns needed for real enterprise AI systems.
YOUR LEVEL
BeginnerIntermediateAdvancedExpert
WHAT YOU WILL BE ABLE TO DO
The skills you will have by the end of this course.
- Build production-grade AI applications in Java using Spring Boot and Spring AI
- Design and implement RAG pipelines with embeddings, vector databases, and reranking
- Build tool-calling agents and orchestrate multi-agent workflows using MCP
- Architect resilient, secure, and observable AI systems for enterprise use
- Apply model routing strategies across local, open-source, and multi-provider LLMs
- Understand and implement the mathematics behind neural networks and transformers
- Build a neural network and a small GPT-style language model from scratch
- Fine-tune, quantize, and deploy open-source models, including multimodal use cases
CONDENSED SYLLABUS
See the structure without reading a textbook.
Modules stay collapsed for quick scanning. Open any module to inspect its topics.
01Introduction to AI & LLM Engineering6 lessons+
AI, Machine Learning & Generative AI Foundations
What Are LLMs? Models, Providers & Tokens
Context Windows, Temperature & Sampling
Prompt Engineering & Message Roles
Prompt Templates & Structured Output
Spring AI, ChatClient, Memory & Streaming
02Building AI Applications with Java6 lessons+
Spring Boot + Spring AI Application Setup
Structured AI Responses & Conversation Management
Tool Calling and Tool Design
External APIs as Read & Write Tools
Embeddings, Semantic Search & Vector Databases
PostgreSQL + pgvector and Basic RAG
03RAG & AI Data Engineering6 lessons+
RAG Architecture & Knowledge Ingestion
Website, HTML, PDF & Database Processing
Data Cleaning, Chunking & Chunk Overlap
Metadata, Embedding Pipelines & Vector Search
Hybrid Search, Query Rewriting & Reranking
Citations, Knowledge Synchronization & RAG Evaluation
04MCP, Agents & AI Workflows6 lessons+
Model Context Protocol Architecture, Client & Server
MCP Tools, Resources & Enterprise System Integration
What Is an AI Agent? Agent vs Chat vs Workflow
Agent State, Memory, Planning & Reasoning
Agent Loops, Termination & Multi-Agent Communication
Deterministic vs Agentic Workflows & Human-in-the-Loop
05AI System Architecture & Resilience6 lessons+
AI-Native Architecture, Orchestration & Model Gateway/Router
Event-Driven AI, Kafka & Microservice Integration
Sync vs Async and Multi-Tenant AI Platforms
LLM Failure Modes, Timeouts, Retries & Circuit Breakers
Caching, Semantic Caching & Rate Limiting
Reactive AI, Backpressure & Cost/Token Optimization
06AI Security, Evaluation & Observability6 lessons+
AI Threat Model: Prompt Injection, Jailbreaking & RAG Poisoning
Tool Abuse, Authorization, Least Privilege & Data Privacy
Guardrails, Input/Output Validation & Secure Agent Design
AI Evals: Groundedness, Relevance, Correctness & Hallucination Detection
Tool-Calling & Agent Evaluation, Regression Testing
AI Tracing with OpenTelemetry, Micrometer, Prometheus & Grafana
07Local Models & Multi-Model Architecture5 lessons+
Open-Source & Local LLMs with Ollama
GPU vs CPU Inference and Quantization
Small vs Large Language Models & Reasoning Models
Model Selection, Routing & Multi-Provider Architecture
Cost-, Latency- and Privacy-Based Routing
08Mathematics & Machine Learning Foundations5 lessons+
Linear Algebra: Vectors, Matrices & Cosine Similarity
Probability, Statistics & Distributions
Calculus & Optimization: Gradients and Gradient Descent
Supervised & Unsupervised Learning, Regression & Classification
Overfitting, Regularization & Model Evaluation Metrics
09Neural Networks from Scratch6 lessons+
Artificial Neurons, Weights, Bias & Activation Functions
Forward Propagation and Loss Functions
Backpropagation and the Chain Rule in Neural Networks
Weight Initialization and Optimization
Building a Neural Network from Scratch
Training with PyTorch and GPU Training
10Building an LLM from Scratch6 lessons+
Tokenization, Vocabulary, Token IDs & Embeddings
Self-Attention: Query, Key, Value & Attention Scores
Multi-Head Attention and Transformer Blocks
Decoder-Only Transformers and Next-Token Prediction
Building a Tokenizer, Attention & a Small GPT-Style Model
Training the Model and Generating Text
11Fine-Tuning, Computer Vision & Multimodal AI5 lessons+
Pretraining, Instruction Tuning & Post-Training
Supervised Fine-Tuning, LoRA & Quantization
Fine-Tuning Open Models, Serving & Deployment
Computer Vision: CNNs, Image Classification & Vision Transformers
Multimodal AI: Vision-Language Models and Multimodal RAG
UPCOMING GROUPS
Choose the cohort you can actually attend.
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Class hours
180 hours
Hybrid- Schedule
- To be announced
- Format
- Hybrid
- Duration
- 24 weeks · 180 hours
- Language
- Confirm with advisor
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Explore graduate resultsAPPLICATION THROUGH INGRESS PORTAL
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SELECTED TRAININGAI for Java DevelopersHybrid
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