I will build mixture of experts, cot reasoning, quantization llms

Most people building "LLM projects" are calling OpenAI's API and wrapping it in a chatbot UI. That's not LLM engineering that's plumbing.

I work under the hood: architecture, reasoning, and optimization. If you need something beyond a prompt wrapper, this is the gig.

What I actually build:

Mixture of Experts (MoE) sparse expert routing, gating networks, load-balancing losses, custom MoE layers built from scratch or fine-tuned on top of existing architectures

Chain-of-Thought Reasoning CoT/ToT prompting pipelines, self-consistency decoding, reasoning-distillation into smaller models, step-verification and reward-guided reasoning

Quantization INT8/INT4 quantization, GPTQ, AWQ, QLoRA, GGUF conversion for local/edge deployment, benchmarking accuracy-vs-speed tradeoffs before you commit to a setup

Also covered: fine-tuning (LoRA/QLoRA), model compression, inference optimization, and deployment on constrained hardware.

Why this matters:

A model that reasons well but can't run on your hardware is useless. A quantized model that's fast but dumb is worse. I build for the actual constraint you're solving cost, latency, accuracy, or all three not just whatever's trending on Twitter.