Generative AI & LLM Systems Engineering Track
From prompt engineering and fine-tuning to production RAG pipelines, vector embeddings, and autonomous multi-agent systems.
Curated by Marcus Chen
Principal AI Systems Architect • ex-Microsoft
Full unrestricted access to technical handbooks, architecture schemas & GitHub projects.
What You Will Accomplish in This Track
Every module builds directly towards industry-level competencies verified by senior hiring managers.
Master Retrieval-Augmented Generation (RAG) with hybrid search and reranking
Fine-tune open-weight models (LLaMA 3, Mistral) using LoRA / QLoRA on custom datasets
Build autonomous multi-agent workflows with LangGraph and AutoGen
Deploy self-hosted LLM endpoints with vLLM, TensorRT-LLM, and Docker
Implement rigorous evaluation metrics, guardrails, and latency optimization
Module-by-Module Detailed Curriculum
Structured into hands-on sprints with clear milestones and code deliverables.
Advanced prompting heuristics, JSON Schema enforcement, function calling, tool use, and context window optimization.
Real Production Capstone Projects
Build, clone, and deploy these applications to showcase on GitHub and resume submissions.
OmniRAG: Multi-Modal Legal Document Analyst
Hybrid vector + keyword RAG platform scanning 10,000+ page contracts with semantic clause cross-referencing.
CognitiveDesk: Ticket Triage & Resolution Copilot
Multi-agent swarm that crawls staging apps, detects visual regressions, and generates pull requests with bug fixes.
Marcus Chen
Principal AI Systems Architect • ex-Microsoft
Pioneered enterprise LLM orchestration frameworks at Microsoft Research. Author of widely-cited papers on multi-agent consensus algorithms.
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