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Explore AI Engineering Full Roadmap vs 100-Day Sprint: In-Demand Skills, Job Market Data & Curriculum with 402,646 views, 4,960 likes, and 451 comments. Experience the impact of this video content that has captured audience attention.

This session breaks down the role of an AI Engineer—focusing on developing, evaluating, and deploying production LLM-based applications rather than training base foundation models from scratch. We analyze real-world hiring trends, demand shifts, and labor market data from the Quess India AI Workforce Report, demonstrating why hands-on expertise in RAG, Agentic AI, and system evaluations outpaces traditional data science demand. You will receive a walkthrough of two learning roadmaps: an exhaustive 16-level, 286-session curriculum covering everything from software engineering fundamentals to system design and LLMOps, alongside a high-velocity 100-day accelerated sprint designed to teach the essential 80% through application-first development. 📂 Interactive Roadmap: https://ai-engineer-roadmap-2026.vercel.app/ This lecture was taken live for insiders, join here: https://youtu.be/j_G30FLmCcw 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at [email protected] Chapters: 00:00 - Introduction & Vision for the AI Engineering Roadmap 02:20 - Defining the AI Engineer Role (Builder vs. Model Trainer) 06:08 - Why AI Engineering is the Most In-Demand Tech Role 09:12 - Industry Data: Google Trends & The Quess India AI Workforce Report 14:40 - Fastest Growing AI Roles: Agentic Developers, RAG Leads & FDEs 17:00 - The Future of AI Jobs: What Gets Automated vs. What Expands 21:40 - Roadmap Architecture: Comprehensive (Full) vs. Accelerated (100 Days) 26:30 - Level 0: Software Engineering Fundamentals (Python, APIs, Docker, SQL) 34:55 - Level 1: LLM 101 (Tokens, Pre-training, Reasoning & Inference Mechanics) 42:38 - Level 2: Working with LLM APIs & Orchestration (LangChain, Tool Calling) 47:20 - Level 3: Developer-Side Prompt Engineering & Output Constraints 49:50 - Level 4: Retrieval-Augmented Generation (Basic to Advanced RAG) 53:20 - Level 5: AI Agents (React, MCP, Memory, LangGraph & Sandboxing) 01:01:00 - Level 6: Context Engineering & Harness Architecture 01:05:58 - Level 7: LLM & Application Evaluations (Offline, Online & Regression) 01:08:55 - Level 8: AI Security, Guardrails & Red Teaming 01:12:28 - Level 9: AI System Design (Model Routing, Latency & Chip Huyen's Book) 01:15:55 - Level 10: LLMOps (Experiment Tracking, CI/CD, Containerization) 01:19:12 - Levels 11 to 13: Managed Platforms, Multimodal Systems & Fine-Tuning 01:24:12 - Levels 14 & 15: Capstone Projects & Targeted Interview Preparation 01:27:18 - The Accelerated 100-Day Roadmap (Consolidating Evals & Security) 01:36:45 - Summary & How to Use the Interactive Roadmap Tool

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