5 Senior-Level AI Projects Video Timestamps & Chapters for Interviews
[0:00] Introduction: The Demo Project Trap The video opens by highlighting how impressive-sounding chatbot projects can lead to tough interview questions about scaling, security, cost, and reliability, motivating the need for production-grade projects. [1:10] Course Promotion: System Design 3.0 A sponsored segment introducing AlgoCamp's System Design 3.0 cohort, covering LLD, HLD, and AI engineering topics like scalable RAG, agentic systems, and observability. [3:10] Project 1: Enterprise-Grade RAG Search Discusses building a RAG system with access control, incremental indexing, hybrid search, and evaluation metrics like recall and citation accuracy, addressing real-world enterprise challenges. [6:10] Project 2: Agentic Customer Support Explores designing an AI agent system for customer support, covering single vs. multi-agent architectures, long-running flows, human-in-the-loop interrupts, and scaling for concurrent users. [8:30] Project 3: Two-Stage Recommender System Covers building a product recommender with candidate generation and ranking stages, tackling cold start problems, fallback strategies, latency budgets, and A/B testing for model evaluation. [10:40] Project 4: LLM Gateway and Routing Describes an LLM routing infrastructure with smart query-to-model assignment, rate limiting, fallback models, caching, and observability metrics like P95 latency and cost per request. [12:50] Project 5: Model Serving and Inference Focuses on deploying open-source models with vLLM, continuous batching, KV cache management, and handling large requests through scheduling, prefill/decode separation, and GPU optimization. [14:30] Conclusion: Encouragement to Build Wraps up by encouraging viewers to pick one project, leverage AI tools for learning, and share feedback or ideas in the comments, emphasizing readiness for senior engineering roles.
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