Telemetry in,coachingdecisions out.

Game telemetry to coaching decisions: playstyle classification, patch-aware retrieval, and agentic tooling on top of official data APIs.

Top 25

Riot x AWS hackathon, globally

25+

playstyle classifiers in production use

2 wks

to earn official Riot data API access

About me

I am Vansh Bordia, an ML engineer focused on the unglamorous middle: the feature pipeline, the retrieval layer, the eval harness, the serving path. A model nobody can query in under half a second is not a product.

My work has shipped as a domain-tuned Gemma layer with RAG over live data, agentic harnesses routing tools and models, and ML pipelines that keep learning as new matches land. Official Riot data API access in two weeks, not two quarters.

Why me

I build the layer between a trained model and a real user: features, retrieval, eval harnesses, serving paths with honest latency budgets, and pipelines that keep improving as new data lands.

I build the layer between a trained model and a real user. A VALORANT-specific Gemma layer with RAG over live patch data, agentic harnesses routing tools and queries, ML pipelines that evolve as matches come in, and eval loops that tell you when a change actually regressed something.

VALORANT-specific Gemma layer with RAG over live patch data

25+ playstyle classifiers feeding real coaching staffs

Official Riot data API access earned in 2 weeks, not 9 months

Role stack

Python / PyTorch / LLM Fine-Tuning / RAG Pipelines / Agentic Systems / Vector Databases / Feature Engineering / Model Evaluation / MLflow / Model Serving / FastAPI / MLOps