RESUME

Vetriselvan R

AI / ML Engineer · Researcher · 2nd-year CS + AI

Delhi NCR, India

Summary

2nd-year CS+AI undergrad working at the intersection of RL and systems — GRPO for compiler codegen, agentic LLM workflows, and spatio-temporal forecasting. Top 4 at IIIT-H × IIT Delhi × IBM AISEHack 2026. Meta PyTorch OpenEnv Hackathon Finalist.

Education

B.Tech — Computer Science and Artificial Intelligence

Newton School of Technology, Rishihood University · Focus: Deep Learning, Reinforcement Learning, Agentic Systems · CGPA: 8.1 / 10

Aug 2024 – May 2028 (expected)

Delhi NCR, India

Projects

ARM-Gym — RL Compiler Optimizer

2025 — Present · Core Contributor (3-person team)

PythonGRPOQwen2.5-Coder-7BLoRAHuggingFace TRLLLVM-MCAQEMUDocker

›GRPO + LoRA fine-tune: Trained Qwen2.5-Coder-7B with LoRA (r=32) under group-relative policy optimization across 649 kernel variants; assembly correctness lifted from 19% to 70%.
›Three-stage verifier: GAS for syntax, QEMU for semantic correctness over 20 adversarial inputs per kernel, LLVM-MCA for cycle-throughput rewards.
›Result: Best measured speedup of +14.5% over clang-21 -O3 on LLVM-MCA estimates.

SRE Incident Responder

Mar 2026 · Meta PyTorch OpenEnv Hackathon × SST · Finalist

PythonFastAPIPydantic v2RedisPostgreSQLDockeropenenv-coreWebSocketMCP

›OpenEnv-compatible RL environment that spins up a real microservice cluster (live Redis and PostgreSQL, not mocks) and challenges agents to debug production outages end-to-end.
›Environment: Five escalating incident tiers from OOM-killed restarts to identifying leaking key patterns amid deliberate red herrings; dense reward shaping (+0.2 diagnosis, +0.8 correct resolution) with penalties for destructive actions.
›Live state grading: Validators query actual infrastructure state instead of mocked traces; multi-protocol access via REST, WebSocket, and MCP (JSON-RPC) for cross-framework agents.

PM2.5 Spatio-Temporal Forecasting

Apr — May 2026 · Team Lead · IIIT-H × IIT Delhi × IBM AISEHack · Top 4

PyTorchConvGRUU-NetPinball LossNumPyPandas

›Architecture: ConvGRU temporal encoder fused with a U-Net spatial decoder for gridded forecasting; predicts PM2.5 concentrations 6 hours ahead.
›Tail-aware training: Dual-model ensemble with pinball loss at the 0.90 quantile biased predictions toward the upper tail, improving recall on extreme pollution spikes.
›Result: Final leaderboard score 0.8834; held on both stable-baseline and spike regimes.

Agentic Credit Risk Scoring — LangGraph + RAG

Oct — Dec 2025

PythonLangGraphLangChainChromaDBGroq Llama 3.3 70Bscikit-learnStreamlit

›Cost-sensitive ML: 100+ behavioral features; class weighting lifted Medium-Risk recall from 0.41 to 0.65 at 80.4% accuracy on a 10,268-sample test set.
›Agentic RAG: LangGraph workflow (risk-scoring → retrieval → report generation) over ChromaDB; Llama 3.3 70B (Groq) grounds each recommendation in retrieved regulatory text.
›Live demo: crs.streamlit.app

Skills

Languages

PythonTypeScriptC++SQL

ML & DL

PyTorchTensorFlowscikit-learnNumPyPandasTransformersHuggingFace TRLLoRA

RL & LLM Tooling

GRPOPPOLangGraphLangChainRAGChromaDBOpenEnvMCPLLVM-MCAQEMU

Infrastructure

FastAPIStreamlitDockerGitGitHub ActionsMongoDBNext.jsReact

Certifications

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Machine Learning·Columbia University (Columbia+)

Supervised & unsupervised ML, linear algebra, neural networks, Transformers.

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Prompt Engineering & Programming with OpenAI·Columbia University (Columbia+)

LLM prompt design, LangChain integration, controlled text generation.

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Generative AI for Data Science·Microsoft

Generative AI concepts, LLM applications, responsible AI in data science workflows.

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Back-End with Node.js & Express·IBM

Server-side development, REST API design, middleware architecture.

Vetriselvan R

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Vetriselvan R