As an AI Infrastructure Engineer, you must build these projects. 1.) High-Throughput Serving Engine Tuning Build: Deploy vLLM or SGLang, tuning chunked prefill, continuous batching and max-num-seqs for a specific GPU architecture. Why: Default configs waste 60% of VRAM. Squeezing the hardware is the baseline job. 2.) KV-Cache Prefix Routing Proxy Build: A load balancer that hashes incoming system prompts and routes requests to the specific replica holding that KV-cache block in memory. Why: Reused context is free speed. This alone can slash Time-To-First-Token (TTFT) by 80%. 3.) Custom Fused Triton Kernel Build: Write a fused RMSNorm or RoPE (Rotary Position Embedding) kernel in Triton and benchmark it against the native PyTorch implementation. Why: Python overhead and memory reads kill inference. Fused kernels save milliseconds that compound at scale. 4.) Speculative Decoding Pipeline Build: A serving setup where a local 2B "draft" model generates tokens in parallel and a 70B "target" model verifies them in a single forward pass. Why: 2x decode speed at zero quality loss is the closest thing to a free lunch in inference. 5.) Disaggregated Prefill/Decode Cluster Build: Separate GPU pools where compute-heavy prefills and memory-bound decodes are routed to different hardware optimized for each phase. Why: This is the exact architecture frontier labs use to maximize MFU (Model Flops Utilization) in 2026. 6.) FP8 Quantization & Calibration Harness Build: A pipeline that calibrates a model for FP8 inference, serves it via TensorRT-LLM and measures the exact perplexity drop against throughput gains. Why: Quantization is the only way to fit frontier models on edge GPUs and protect your cloud margins. 7.) Queue-Based GPU Autoscaler (KEDA) Build: An autoscaler that spins up spot GPU nodes based on pending token queue depth and gracefully drains them when the queue empties. Why: Idle H100s burn $3+/hr. FinOps and scheduling are now core infra responsibilities. 8.) Complexity-Routing AI Gateway Build: A proxy that analyzes prompt entropy and token length to instantly route simple queries to a quantized local 8B model and complex reasoning to a frontier API. Why: Unit economics decide if your AI product survives the next API price hike. 9.) Multi-Node Tensor Parallelism Setup Build: Shard a 70B+ parameter model across 4 nodes using NCCL over RDMA/RoCE v2 and profile the exact network bottleneck between GPUs. Why: Single-node inference is dead for frontier models. You must master the interconnect. 10.) Semantic Cache Layer Build: A Redis + Vector DB proxy that intercepts requests, checks for >98% embedding similarity, and returns cached outputs without hitting the GPU. Why: The cheapest and fastest token is the one you never generate. 11.) DCGM Observability & Throttling Dashboard Build: A Grafana stack tracking GPU temp, SM occupancy, VRAM OOMs and PCIe TX/RX to catch silent hardware throttling. Why: You cannot fix an SLO violation if your dashboard only shows HTTP 200s while the GPU is melting. 12.) MoE (Mixture of Experts) Load Profiler Build: An expert-parallel deployment tracking token-to-expert routing to identify and fix expert load imbalance and communication overhead. Why: Sparse MoE models are the 2026 standard, and naive routing destroys their latency advantage. 13.) Inference Chaos & Fault Tolerance Suite Build: A harness that injects network partitions, simulated GPU ECC errors, and OOMs to test the cluster's ability to migrate KV-caches and recover. Why: At scale, hardware fails daily. Your architecture must heal itself. 14.) Checkpointing & Weight Streaming Engine Build: A system that streams model weights directly from an S3 bucket to GPU VRAM on cold-start, bypassing local disk bottlenecks. Why: Cold-start latency kills autoscaling. Streaming weights directly to memory is the ultimate optimization. 15.) Public Inference Benchmark Teardown Build: 3 published deep-dives with Pareto curves (Cost vs TTFT vs Throughput) comparing vLLM vs SGLang vs TensorRT-LLM on your specific hardware. Why: Public proof of hardware mastery and methodology gets you hired instantly by top AI labs. Systems that prove you can serve, optimize & scale when silicon meets intelligence. Bookmark & Repost.