Building Systems that
Learn, Adapt & Scale
I build and deploy intelligent systems — from data to production.
About Me
Building real-world AI systems, not just models
I’m a Computer Science student focused on building real-world AI systems — from experimentation to deployment. My work spans intelligent document systems, computer vision, and scalable ML pipelines. I enjoy taking ideas beyond notebooks and turning them into usable, production-ready solutions. I care about performance, scalability, and practical impact — not just model accuracy.
I focus on solving problems end-to-end — from data processing and model design to APIs and deployment. Whether it’s optimizing inference, building retrieval systems, or integrating AI into real applications, I aim to create systems that actually work in the real world.
# local_llm.py
from transformers import pipeline
# Load local LLM
pipe = pipeline(
"text-generation",
model="mistralai/Mistral-7B-Instruct-v0.1",
device_map="auto"
)
# Run inference
response = pipe("Explain AI in simple terms",
max_new_tokens=50,
temperature=1.2)
print(response[0]["generated_text"]) Featured Projects
AI systems I've built from scratch — moving beyond tutorials to solve real-world problems.
IRS – Intelligent Retrieval System
FeaturedBuilt a Retrieval-Augmented Generation (RAG) system to query PDFs using semantic search and LLMs, enabling context-aware and source-grounded answers.
Self-Pruning Neural Network
FeaturedDesigned a neural network with learnable gates to dynamically remove redundant connections during training, achieving high sparsity without accuracy loss.
SRE Monitoring Pipeline (IaC)
FeaturedBuilt an Infrastructure-as-Code system to deploy and configure a monitoring stack using Terraform and Ansible with Prometheus, Grafana, and Alertmanager.
Fault Management (FM) NBI Flow
FeaturedBuilt a production-grade REST API simulating a Network Management System (NMS) Northbound Interface for real-time fault detection, logging, clearing, and auto-simulation of network events using Spring Boot and H2 database.
Real-Time Image Sharpening System
Developed a lightweight image sharpening model using knowledge distillation, achieving real-time performance with significant reduction in model size.
Technical Skills
Technologies and systems I use to build production-ready AI applications
LLM / GenAI
AI / ML
Languages
Web
Tools & DevOps
Cloud
Certifications
Continuous learning in a fast-paced field.
Achievements
Milestones and recognitions along the journey.
Experience
Where I've made an impact.
Software Engineering Intern
- ◆Compressed VGG19 to 50% size using knowledge distillation while preserving 95%+ PSNR/SSIM
- ◆Built multi-threaded real-time inference pipeline with GPU FP16 acceleration (sub-second latency)
- ◆Added structured logging + telemetry for production observability
Let's build something meaningful
From ideas to production-ready systems.
Get in Touch
Have a project in mind or just want to chat about AI? I'd love to hear from you.