AI Engineering & Core Foundations Roadmap
An interactive tracker to balance deep theoretical science with pragmatic architectural delivery.
AI Platform & Integration
Core AI/ML Foundations
AI Platform & Integration
0% Completed
Phase 1: High-Concurrency & Native Frameworks
Months 0 - 3
Java 21 Virtual Threads (Project Loom)
Configure Spring Boot 3.2+ for asynchronous orchestration. Master virtual threads to handle high-concurrency IO AI operations.
Resources:
Oracle Virtual Threads Guide
,
Spring Boot Loom Integration
Spring AI Ecosystem Mastery
Implement native Java components for auto-configuration and declarative tool-calling using standard POJOs.
Resources:
Spring AI Reference Doc
LangChain4j & Native GraalVM Compilation
Build lightweight AI pipelines using LangChain4j. Optimize memory footprints to 50-100MB with GraalVM.
Resources:
LangChain4j GitHub
,
GraalVM Native Image
Phase 2: Context Engineering & Vector Data
Months 3 - 6
Advanced Document Chunking & Parsing
Implement hierarchical and semantic chunking structures to preserve document context before embedding.
Resources:
Pinecone Chunking Strategies
Vector Database Provisioning & Querying
Deploy and optimize localized semantic similarity searches using pgvector, Qdrant, or Milvus.
Resources:
pgvector
,
Qdrant Docs
Phase 3: High-Scale Cloud Compute Engines
Months 6 - 9
Distributed Data Processing
Learn Apache Spark, Ray or Snowflake Snowpark to handle large-scale data transformation for AI pipelines.
Resources:
Apache Spark Docs
,
Snowpark Guide
Phase 4: Agentic Design & Gatekeeping
Months 9+
Autonomous Agent Orchestration
Construct agentic state machines that interpret model loops, parsing tool outputs dynamically.
Resources:
DeepLearning.AI LangGraph Course
Phase 1: Mathematics & Statistical Theory
Months 0 - 3
Linear Algebra & Multivariate Calculus Foundations
Master matrix multiplication, SVD, partial derivatives, and gradient descent intuition.
Resources:
3Blue1Brown Linear Algebra
,
Mathematics for Machine Learning Book
Probability & Inference
Study Bayes' theorem, maximum likelihood estimation, and distributions.
Resources:
MIT 18.05 Introduction to Probability
Phase 2: Classical Machine Learning & Python Toolkit
Months 3 - 6
Scientific Python & EDA
Build fluid data engineering structures with NumPy, Pandas, and Matplotlib.
Resources:
Andrew Ng ML Specialization
,
Pandas Docs
Supervised & Unsupervised Learning Construction
Implement regression, decision trees, SVMs, PCA, and K-Means clustering.
Resources:
Scikit-Learn Tutorials
Phase 3: Deep Learning Architectures
Months 6 - 9
Artificial Neural Networks (ANNs) & PyTorch Ecosystem
Code forward/backward propagation. Leverage PyTorch tensors, autograd, and dynamic dataloaders.
Resources:
DeepLearning.AI DL Specialization
,
PyTorch 60 Min Blitz
Phase 4: Advanced Sequence Modeling & Transformers
Months 9+
Attention Mechanisms & Transformer Architectures
Understand Multi-Head Attention, BERT, GPT variants, and foundational NLP.
Resources:
Attention Is All You Need (Paper)
,
Hugging Face NLP Course
LLM Fine-Tuning & Deployment
Utilize PEFT, LoRA, and QLoRA to fine-tune massive parameter models locally.
Resources:
PEFT Documentation