Project

BHAVESH GUDLANI Builds complete systems

From backend APIs and databases to AI pipelines and production deployments – I build things end-to-end, and I've been doing that across stacks long enough to know what actually works.

Chapter I – Who I Am

Full-stack, AI/ML,
backend, mobile
all of it.

I build complete systems – not just pieces of them. Backend designed, database architected, frontend shipped, AI pipeline running, deployed to production. That end-to-end ownership is what makes the difference between a demo and a product.

My depth spans autonomous AI systems, multi-agent orchestration, and self-healing pipelines as much as it covers Android apps, REST APIs, full-stack web, and desktop with Electron. I pick up whatever a problem demands – and I've demanded enough from myself to cover most of it.

5+
Complete systems built – backend to deployment
0
Year of professional work experience
7+
Industry certifications – Google, AWS, JPMorgan, Deloitte and more
Expanding stack
Mobile Backend Full Stack AI/ML + All
AI/ML Systems Backend & APIs Full-Stack Web Android Dev System Design Cloud & Deploy
Chapter II Projects I've Built
01 / 05
01
Project 01

KYRON

AI Execution System

An autonomous AI system that takes a business goal and figures out how to execute it — end to end.

KYRON Project Preview View Project
What it does

KYRON is a full-stack SaaS platform where you define a business goal and an AI agent network handles the execution. It uses a multi-agent architecture — one agent plans, one monitors progress, one handles recovery when things go wrong. Every decision made is logged and fully auditable.

Key Features
Multi-agent coordination with role-based task delegation
Automatic failure detection and self-recovery
Full decision audit trail — transparent and explainable
Cross-device, omnichannel interface
Stack
LangGraphPythonReact.jsNode.jsFastAPIFirebase
Why it matters: Most AI tools require constant hand-holding. KYRON was built to actually run on its own — give it a goal and step away.
02
Project 02

LLM Engine

Language Model from Scratch

A transformer-based language model built completely from scratch — not fine-tuning, not an API. The real thing.

LLM Engine Project Preview View Project
What it does

Built a transformer-based LLM in PyTorch from the ground up — including a custom tokenizer, embedding layers, multi-head self-attention, positional encoding, and a complete training pipeline.

Key Features
Custom tokenizer trained on domain-specific corpus
Full multi-head self-attention implementation
Optimized PyTorch training loop with custom scheduler
Model reused as inference engine in RAG system
Stack
PyTorchPythonTransformersNumPy
Why it matters: Calling an API doesn't mean you understand what's happening. Building it yourself does.
03
Project 03

Self‑Healing RAG

Retrieval-Augmented System

A RAG pipeline that detects when it's wrong and automatically fixes itself — no silent hallucinations.

Self-Healing RAG Project Preview View Project
What it does

Most RAG systems return confident wrong answers. This one doesn't. After generating a response, it validates the output against retrieved context. If confidence drops below threshold, it rewrites the query and tries again.

Key Features
Per-response confidence scoring against source context
Auto query rewriting on low-confidence outputs
Closed-loop retry without human intervention
Vector search via FAISS + Pinecone hybrid
Stack
PyTorchFAISSPineconeLangGraphChromaDB
Why it matters: Knowing when you're wrong is harder than being right. This system does both.
04
Project 04

Jal Vaani

Water Intelligence Platform

A data intelligence platform for tracking, analyzing, and predicting groundwater levels across India.

Jal Vaani Project Preview View Project
What it does

End-to-end app built to handle water data at scale. Ingests real-time WRIS + NASA readings, runs ML models for anomaly detection and prediction, and surfaces insights through a visual dashboard.

Key Features
Real-time data ingestion from WRIS and NASA sources
ML-based seasonal forecasting and recharge modeling
Fully automated pipeline refreshes post-deployment
Designed with government-scale deployment in mind
Stack
Java + XMLSupabasescikit-learnPythonGithub Actions
Why it matters: Water management in India runs on spreadsheets. This was built to change that.
05
Project 05

SACH-AI

Content Verification System

Multimodal AI that detects deepfakes, fake messages, AI-generated content, and cloned voices — and explains exactly why.

SACH AI Project Preview View Project
What it does

SACH AI analyzes text, images, videos, audio, and screenshots to determine if content is authentic or manipulated. It returns a trust score, risk level, and a clear explanation of the reasoning — not just a label.

Key Features
Deepfake detection in video frames with probability scoring
AI-generated image and cloned voice identification
Scam and phishing pattern detection in text/screenshots
RAG-backed claim verification against trusted sources
Stack
PythonFastAPIOpenCVReact.jsNLP ModelsOCRRAG
Why it matters: Built to prove that multimodal verification can work as one unified pipeline.
Chapter III

Skill Stack

Languages I Know

PythonJavaJavaScriptCC++SQL.NETKotlinPHPTypeScriptXMLAssembly

Frameworks I've Used

PyTorchLangGraphReact.jsNode.jsFlutterOpenCVPandasElectronAngular jsExpress.jsDjangoFramerThymeleafRedisTensorFlowGradle

Tools & Platforms

GitFirebaseGoogle CloudSupabaseAndroid StudioVercelPostmanAWSCloudflareGunicornSpringApache KafkaDockerHerokuRenderRaabitMQGithub ActionsJiraNotion
Chapter IV

Work
Experience

Senior Android Developer
at WowCodes
Oct 2023 – Oct 2024
Worked with stakeholders to understand product requirements and translated them into solid technical decisions – bridging what was needed and what was buildable.
Collaborated across design, product, and backend to ship features end-to-end, improving app stability and UX throughout the engagement.
Built and maintained production apps using MVVM architecture, RESTful APIs, and Firebase – serving real users consistently.
KotlinMVVMREST APIsFirebaseAndroid Studio
Chapter V

Education & Certifications

🎓 Degree
B.Tech in CSE
MIT Vishwa Prayag University, Solapur
Expected 2028
Minor: AI & ML
📚 Diploma
Diploma in CS
S.E.S. Polytechnic, Solapur · 2023
89% – Distinction
🏫 Class 10
I.C.S.E. Board
St. Thomas School, Solapur · 2020
94.4%
Certifications
Chapter VI – Let's Talk

Let's
build
something

Whether it's a job opportunity, a project, or a conversation about something interesting – inbox is open.

Looking for opportunities where I can build real things – AI/ML projects, full-stack systems, challenging engineering problems. If you're building something that needs all-round ownership, let's talk.