If you’re trying to break into AI engineering in 2026 (or level up), this video is my no-fluff breakdown of 5 portfolio projects that actually matter in interviews. I’m not talking about “follow a tutorial, deploy a basic chatbot” work. I mean projects that make a hiring manager look at your GitHub and immediately see that you understand production AI systems—reliability, measurement, evaluation, and the real constraints teams deal with. I walk through: (1) a production-grade RAG system that goes beyond a demo—hybrid retrieval (BM25 + vector), cross-encoder reranking, citation enforcement, and CI-gated evals using a golden dataset; (2) a fully offline local SLM app with Ollama where you benchmark tokens/sec, time-to-first-token, and quality vs speed across multiple models; (3) monitoring and observability for your RAG (tracing, p50/p95 latency, cost per request, citation coverage, failure rate, and regression gating); (4) fine-tuning with LoRA/QLoRA plus DPO preference tuning with clear before/after metrics; and (5) a real-time multimodal app (I’d personally pick voice) with a detailed latency budget and resilience features like timeouts and graceful degradation. Together, these projects tell one cohesive story: you build AI systems that are measurable, dependable, and shippable.