AI Systems Engineer · Agentic AI · Multi-Agent Orchestration
ML Infrastructure · LLM Integration · Low-Level Optimization
Building production-grade AI systems from the ground up — from autograd engines in JAX to 7-layer agentic safety architectures. 1st-year CSE (AI & DS) student with real shipped ML pipelines.
I'm a 1st-year B.Tech CSE (AI & DS) student at Nxtwave Institute of Technologies, Pune, deeply focused on AI systems engineering — not just using models, but understanding how they work at every layer.
From rebuilding Karpathy's micrograd from scratch in JAX to shipping production ML pipelines at CodeAlpha, I work at the intersection of low-level systems and applied LLM engineering.
My current obsession: agentic AI safety, multi-agent orchestration, and making AI systems reliable enough to actually deploy. I documented a real incident where a local AI agent crashed my workstation and proposed a 7-layer fix.
End-to-end stack from low-level optimization to deployed AI products.
Real production systems, shipped and evaluated.
From autograd engines to VLIW optimization — always close to the metal.
Rebuilt Karpathy's entire neural network curriculum from scratch in JAX — micrograd autograd engine (jax.grad, jax.vmap, jax.lax.scan, JIT) and Bigram language model with 228K+ training pairs. No framework abstractions — everything built from first principles.
Implemented KernelBuilder.build_kernel() for a simulated VLIW-style multi-core machine. Reduced execution cycles from 147,734 → ~14,425 — a ~10× improvement — while preserving exact output correctness.
End-to-end credit risk pipeline with imputation, categorical encoding, and mutual-information feature selection. Benchmarked Logistic Regression, Random Forest, and XGBoost — achieved ROC-AUC ~0.91 on held-out test.
Extracted 40-dim MFCC features with Librosa. Trained LSTM achieving ~82% accuracy across 7 emotion classes with a fully reproducible TensorFlow/Keras end-to-end training and inference pipeline.
Empirical research on autonomous AI agent safety — from a real incident.
Documented a real incident where a locally deployed AI agent stack (Ollama + Kimi 2.5 + OpenClaw with a 9-layer PROMETHEUS autonomous system prompt) crashed a personal workstation. Proposed a 7-layer PTF safety architecture as a response.
Open to remote AI/ML engineering roles, research collaborations, and interesting problems in agentic AI.