SLM Engineering: End-to-End

Who is this for?
Built for engineers who want to go deep.
Engineers transitioning into LLM engineering, NLP, or enterprise AI
Developers building AI-powered products - chatbots, search, document Q&A
Engineers who want to go beyond API calls - to building and training models
Researchers who need production engineering depth alongside theory
What you will achieve
Leave production-ready.
Enterprise AI interview question
"Design an enterprise SLM system that fine-tunes on private data, supports RAG with permission-aware retrieval, and deploys on-premise with sub-100ms latency. Walk me through the architecture."
Asked at companies building enterprise AI products. You will have a complete answer.
Build, train, and deploy enterprise SLMs from scratch - transformers, SFT, RLHF, quantization
Design efficient training pipelines with LoRA, QLoRA, and distributed training
Implement RAG, tool calling, and agentic workflows with small models
Deploy models with vLLM, llama.cpp, and production inference servers
Build industry-level portfolio projects from hands-on capstone work
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