I work across the modern AI stack: machine learning, deep learning, LLMs, RAG, agents, model infrastructure, inference optimization, hardware acceleration, AI security, blockchain, and ZKML.
Right now I am building practical resources and systems that help engineers learn, evaluate, and ship modern AI products with a clear path from fundamentals to mastery.
Modern AI Engineer Roadmap is a beginner-to-master roadmap for becoming a modern AI engineer. The roadmap is designed around staged learning, deep dives, examples, practice work, local exams, and portfolio-ready projects. It covers:
- AI and ML fundamentals
- Deep learning and PyTorch
- LLMs, prompting, fine-tuning, and evaluation
- RAG, AI applications, and guardrails
- AI agents, tools, MCP, memory, and observability
- Model infrastructure, deployment, reliability, and cost control
- Inference optimization, serving engines, CUDA, and hardware acceleration
- AI security, blockchain, smart contracts, ZK, and ZKML
Roadmap site: https://aiZKP.github.io/modern-ai-engineer-roadmap/
If the roadmap helps you, please give the repository a star 🌟 https://github.com/aiZKP/modern-ai-engineer-roadmap 🌟
- Building useful AI systems, not just demos
- Designing reliable RAG and agent workflows
- Evaluating model behavior, failure modes, latency, and cost
- Connecting AI product work with infrastructure and deployment
- Studying optimization, hardware-aware inference, and verifiable AI
My goal is to build a complete modern AI engineering path that connects research ideas, production patterns, and hands-on projects into one clear learning system.