Ankit Dahal

Frontier AI is a full stack problem: compute and distributed systems at the base, architecture and pretraining above it, post-training and evaluation on top of that, retrieval and tools wired in, and agents acting in real environments at the edge. A common idea expressed by frontier AI leadership is that the best engineers, who ship, can reason across multiple layers—tracing a broken eval down to a data-mix decision, a flaky agent back up to a sampling parameter—with the first-principles depth to not just fix it at the layer that’s broken, but improve it as well. The generalist-with-depth profile is what I have been building towards.

The projects below are arranged to map cleanly onto that stack, building on years of key developments in the space from 2018 to 2026. From basic distributed gpu pretraining on my own transformer implementation, to instruction tuning with LoRA/PEFT, agentic RAG, and most recently building & training multi-modal agents complete with a custom environment harness, efficient inference serving, high-quality seed data gathering and synthetic data generation.