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I am an AI/ML Engineer focused on architecting intelligent systems that bridge advanced research with real-world applications. My expertise covers the entire machine learning lifecycle—designing neural architectures, building training pipelines, and deploying models that power large-scale, high-impact solutions.
I specialize in translating cutting-edge research into production-ready systems, with experience in optimizing transformer models for efficiency, developing robust intent and NER models for assistants, and scaling distributed training for foundation models.
My approach blends strong mathematical fundamentals with pragmatic engineering practices, ensuring that every system I build is both high-performing and maintainable. I thrive on creating AI solutions that are not just innovative, but also practical, reliable, and impactful.
My research focuses on efficient AI model architectures, aiming to push the limits of performance while drastically reducing computational costs. I investigate novel compression methods, sparse attention mechanisms, and dynamic neural networks, alongside hardware-aware architecture design.
By bridging cutting-edge research with industrial deployment, my work ensures that theoretical innovations translate into scalable, production-ready AI systems that deliver real-world impact.
mindmap
root((AI/ML Research))
Efficient Models
Sparse Attention Mechanisms
Knowledge Distillation
Quantization & Pruning
Hardware-Aware Neural Architecture Search
Large Language Models
Parameter-Efficient Fine-Tuning (PEFT)
Instruction Tuning
RLHF & DPO
Multi-Modal LLMs
Production ML
Model Serving Optimization
Online Learning Systems
Feature Store Architecture
ML Observability & Monitoring
Emerging Areas
Diffusion Models
Mixture of Experts
Neuromorphic Computing
Federated Learning
gantt
title AI/ML Research & Development Roadmap 2025
dateFormat YYYY-MM
section Research
Efficient Transformers Paper :2025-01, 3M
Mixture of Experts Study :2025-04, 4M
Neuromorphic Computing :2025-08, 4M
section Open Source Projects
NAS Framework v2.0 :2025-01, 2M
LLM Toolkit Enhancement :2025-03, 3M
Production ML Platform Features :2025-06, 4M
section Learning & Skills
Advanced Rust for ML :2025-01, 2M
Triton GPU Programming :2025-03, 2M
System Design Mastery :2025-05, 3M
const shivam = {
philosophy: "Code is poetry, algorithms are art",
currentlyLearning: ["Mixture of Experts", "Diffusion Models", "Neuromorphic Computing"],
collaboration: "Open to research partnerships and innovative ML projects",
funFact: "I optimize neural networks faster than I optimize my coffee intake ☕"
};"Building intelligence, one tensor at a time" ⚡


