Rudransh Shekhar

Rudransh Shekhar

Founder @ mySphere
EX - AI/ML Intern @ IMD
Cloud Infrastructure & Distributed Systems

B.Tech Computer Science student at VIT Chennai, Founder & Lead Developer at mySphere, and AI/ML Intern at the India Meteorological Department (IMD).

My interests lie at the intersection of cloud infrastructure, distributed systems, artificial intelligence, and software engineering. As the founder of mySphere, I design and develop scalable software products ranging from distributed personal cloud infrastructure and cross-platform applications to AI-powered and financial technology systems. My work involves backend engineering, cloud-native architectures, system design, DevOps, and secure application development.

At IMD, I work on AI-driven weather forecasting systems, contributing to the development, fine-tuning, and optimization of deep learning models trained on large-scale atmospheric datasets. My experience includes model inference, distributed training, high-performance computing (HPC), data engineering, and the development of Agentic AI applications powered by Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG).

Beyond research and product development, I enjoy building systems that combine backend engineering, machine learning, cloud technologies, and real-world problem solving. My goal is to create scalable, reliable, and impactful technology that bridges the gap between research and practical applications.

Work Experience

Aug 2025 - Present

India Meteorological Department (IMD)

Ministry of Earth Sciences, Government of India

AI/ML Intern | Internship | Mausam Bhawan, New Delhi, India | May 2026 - June 2026
  • Fine-tuned Huawei's Pangu-Weather model on approximately 3.5 years of IMD GFS data (2023-2026), adapting a globally pre-trained model to regional meteorological forecasting requirements.
  • Optimized code, data-loading, and distributed training workflows, reducing Pangu-Weather training time from about 20 minutes to about 8 minutes per epoch.
  • Contributed to Pangu-Lite, an internally implemented lightweight weather forecasting model, resolving critical training issues and implementing Distributed Data Parallel (DDP) training on over 40 years of ERA5 reanalysis data (1979-2024).
  • Reduced Pangu-Lite training time from about 32 hours to about 5 hours per epoch through distributed computing and pipeline optimization.
  • Built components for an Agentic AI weather assistant using Retrieval-Augmented Generation (RAG), meteorological data sources, and local/cloud-hosted LLMs for natural-language weather forecasting and query processing.
  • Implemented location-aware weather retrieval pipelines using IMD forecasting systems and Mausamgram datasets, integrating Zephyr and Gemma models for contextual responses.
  • Developed PBS job scripts and DDP training workflows using NCCL for multi-GPU and multi-node training across IMD's Arunika High Performance Computing cluster.
  • Refactored fragmented training codebases into production-grade workflows for training, inference, validation, forecasting, checkpointing, early stopping, experiment tracking, geospatial visualization, and model evaluation.
  • Worked with Linux (Red Hat), HPC environments, SSH-based infrastructure, PBS scheduling systems (qsub, qstat), and research-oriented machine learning workflows.
AI/ML Research Collaborator | Part-time | Remote | August 2025 - April 2026

Collaborative tasks under Dr. Ramesh Ragala (IMD)

  • Collaborated on AI-driven weather forecasting research using the Pangu-Weather model.
  • Reverse engineered and adapted the open-source Pangu-Weather implementation, fixing model conversion workflows to convert ONNX models into PyTorch (.pth) checkpoints.
  • Validated converted models by comparing ONNX and PyTorch inference outputs, achieving near-identical predictions with negligible numerical error.
  • Developed and optimized auto-regressive forecasting pipelines for multi-step weather prediction, generating forecasts up to 10 days ahead.
  • Worked with GRIB, GRIB2, and NetCDF (NC) meteorological data formats, building scripts for data conversion, preprocessing, validation, and workflow automation.
  • Supported model inference and experimentation across Google Colab TPU infrastructure and institutional HPC systems using Linux, SSH, PuTTY, and command-line workflows.
  • Contributed to optimization, troubleshooting, and validation of large-scale atmospheric data pipelines, model inference workflows, and high-performance computing experiments.
Aug 2025 - Present

mySphere

Startup | Available at: mysphere.co.in

Founder & Lead Developer | Full-time | Remote | Aug 2025 - Present
  • Founded and lead mySphere, an independent software startup focused on cloud infrastructure, artificial intelligence, distributed systems, and cross-platform product ecosystems (mysphere.co.in).
  • Own product architecture, backend engineering, infrastructure management, deployment pipelines, and end-to-end software development across all mySphere products.
  • myCloud: Architected and built a high-performance distributed personal cloud ecosystem decoupling file metadata from storage nodes with live WebSockets relay, CLI (mycloudctl), Python SDK (mycloud-sdk), Web PWA, Desktop Node GUI, and OCI cloud infrastructure.
  • myTrade: Engineered an autonomous multi-agent AI market intelligence platform for Indian equity markets (NSE/BSE), combining quantitative indicator analysis, 40+ LLM jury consensus, local ChromaDB vector RAG, friction tax modeling, and remote Telegram HITL execution.
  • InferenceOS: Created an open-source, hardware-agnostic adaptive operating system for local LLM inference built on llama.cpp C++20 kernels, dynamic microbatching, live hysteresis VRAM offload migration, and dual OpenAI/Ollama REST API servers.
  • myCam: Developed a real-time smart IP security streaming platform with React 19, Socket.IO, WebRTC, Express 5, MySQL, and computer vision motion detection with interactive Canvas activity zones and two-way audio.
  • mySync: Built a real-time cross-device clipboard & notification relay platform with WebRTC P2P mesh data channels and Llama 3 AI summary integration across React Native, React + Vite, and Node.js.
  • myTune: Developed a cross-platform music streaming and playlist management application with high-resolution playback, intelligent queue management, and ecosystem account integration.
  • Designed reusable platform components, shared authentication services (OAuth/2FA), and microservices to optimize scalability, maintainability, and interoperability across the mySphere product suite.

Projects

AI/ML Projects

Pangu-Weather Fine-Tuning IMD Aug 2025 - May 2026 Completed

Regional Weather Forecasting

Deep Learning PyTorch Weather Modelling Machine Learning Model Inference Distributed Training
20 mins -> 8 mins Reduced Training Time
3.5 YearsTrained on IMD GFS Data
10 DaysAutoregressive Forecast
  • Fine-tuned Huawei's Pangu-Weather model on approximately 3.5 years of IMD GFS data (2023-2026), adapting a globally pre-trained weather forecasting model for regional meteorological prediction.
  • Developed preprocessing pipelines for large-scale meteorological datasets, including GRIB-to-NetCDF conversion, variable transformations, humidity parameter handling, dataset validation, and quality assurance.
  • Optimized training and inference workflows, reducing training time from approximately 20 minutes to 8 minutes per epoch through code, data-loading, and distributed training improvements.
  • Implemented auto-regressive forecasting pipelines capable of generating multi-day forecasts by recursively feeding model outputs back as inputs.
  • Built model evaluation workflows to compare forecasts against observed weather data and existing operational forecasting systems across multiple variables, pressure levels, and forecast horizons.
  • Developed geospatial visualization and analysis pipelines for meteorological variables using GIS datasets and weather forecasting outputs.

Links & Resources

Pangu-Lite IMDMay 2026 - June 2026 Completed

Distributed AI Weather Forecasting Model

Distributed Computing Deep Learning HPC NCCL Model Training ERA5 Data Forecasting
32 hours -> 5 hours Reduced Training Time
40+ YearsTrained on ERA5 Data
Multi-GPU Multi-Nodal DDP Setup
  • Contributed to the development of an internally implemented lightweight weather forecasting model trained on more than 40 years of ERA5 reanalysis data (1979-2024).
  • Resolved critical training issues and refactored fragmented codebases into unified production-grade training and inference pipelines.
  • Implemented Distributed Data Parallel (DDP) training using NCCL and developed PBS job scripts for efficient multi-GPU, multi-node execution on IMD's Arunika High Performance Computing cluster.
  • Reduced training time from approximately 32 hours to 5 hours per epoch through distributed computing, pipeline optimization, and infrastructure improvements.
  • Built end-to-end training, inference, validation, checkpointing, experiment tracking, logging, and forecasting workflows for large-scale weather prediction experiments.
  • Contributed to architectural experimentation involving patch embeddings, ensemble inference, error-correction approaches, and variable cross-attention mechanisms for weather forecasting models.

Links & Resources

InferenceOS Open-Source 2026 - Present Active Development

Hardware-Agnostic Adaptive Operating System for Local LLM Inference

Python 3.10+ C++20 CUDA & ROCm Vulkan & Metal llama.cpp Engine FastAPI (OpenAI & Ollama API) SQLite Telemetry TUI CLI
Zero OOMDynamic Microbatch Prefill
Live MigrationHysteresis Layer Offload
Dual APIOpenAI & Ollama Server

Project Overview

InferenceOS is an open-source, hardware-aware operating system and control plane designed for local Large Language Model (LLM) inference. Built on an architectural fork of llama.cpp, it bridges high-performance C++ tensor execution kernels with intelligent Python runtime orchestration to dynamically profile hardware, optimize layer placement, execute live hysteresis migrations under VRAM pressure, compress KV caches, and stream responses via dual OpenAI/Ollama REST APIs.

Features

  • Hardware Auto-Profiler: Multi-vendor detection engine profiling CPU, NVIDIA CUDA, AMD ROCm, Apple Metal, Intel Arc, and integrated GPUs to auto-generate optimal execution plans.
  • Dynamic Microbatch Scheduler: Real-time prefill batching using multi-heuristic scoring (VRAM headroom, prompt TPS, GPU occupancy, PCIe bandwidth) to eliminate out-of-memory crashes.
  • Live Hysteresis Layer Migration: Real-time offload coordinator that dynamically migrates layers between dGPU, iGPU, and CPU during active inference under VRAM pressure.
  • KV Cache Manager & Eviction: Memory optimizer supporting FP16/Q8_0/Q4_0 cache quantization and attention-sink eviction policies (H2O Heavy-Hitter Oracle, StreamingLLM, LRU, FIFO).
  • Runtime Learning Database: Persistent SQLite telemetry recorder that tracks execution metrics (TTFT, prompt t/s, evaluation t/s) and predicts optimal model configurations via ML regression models.
  • Dual REST API Server & 21-Command TUI: High-throughput FastAPI HTTP server supporting OpenAI (/v1/*) and Ollama (/api/*) endpoints with streaming SSE, accompanied by a 21-command interactive TUI CLI.

Links & Resources

myTrade mySphere May 2026 - Present In Development

Autonomous AI Market Intelligence & Trading Platform

Multi-LLM Consensus ChromaDB (RAG) SQLite Upstox API Streamlit UI Telegram Bot AsyncIO
Multi-LLM Jury40+ Models Consensus
Quant EngineMath Risk Veto
HITL & AutoTelegram Control

Project Overview

myTrade is an autonomous, multi-agent AI market intelligence and trading system designed for the Indian equity markets (NSE/BSE). It combines quantitative indicator analysis, multi-model LLM jury consensus, local vector RAG memory, and strict mathematical risk guardrails with a remote Human-in-the-Loop (HITL) control interface.

Features

  • Multi-Model Consensus Engine: Queries a configurable pool of independent LLMs concurrently (40+ model roster across Groq, Nvidia, Mistral, Google, Cerebras) with dynamic multi-key API cycling to benchmark qualitative sentiment without single-model hallucinations.
  • Mathematical Risk Veto Layer: Deterministic quantitative rules evaluating macro market regimes, price action, and momentum to serve as a pre-AI guardrail layer that can veto qualitative entries.
  • Hybrid Data & RAG Memory System: Combines SQLite structured price storage with ChromaDB vector embeddings for corporate disclosures, regulatory filings, and market news.
  • Google OKF Context Assembly: Synthesizes price metrics, technical indicators, and vector RAG context into standardized Open Knowledge Format (OKF) payloads for model evaluation.
  • Friction & Tax Modeling Engine: Calculates transaction friction costs including STT, GST, SEBI charges, brokerage, and stamp duty to verify net capital deployment efficiency.
  • Interactive HITL & Telegram Control: Secure 4-message Telegram dashboard allowing real-time monitoring of AI juror votes, news context, and tax breakdowns with interactive trade approval, rejection, or veto override buttons.
  • Execution & Broker Integration: Features a local SQLite paper ledger for shadow portfolio tracking alongside Upstox API integration and configurable auto-approval switches.

Agentic AI Weather Assistant IMD May 2026 - June 2026 Completed

LLM-Powered Meteorological Query System

Retrieval-Augmented Generation (RAG) Large Language Models (LLM) Natural Language Processing (NLP)
RAGArchitecture
Zephyr/GemmaLocal & Cloud LLMs
MausamgramWeather Data
  • Contributed to the development of an Agentic AI weather assistant integrating Retrieval-Augmented Generation (RAG), meteorological data sources, and Large Language Models (LLMs) for natural-language weather forecasting.
  • Implemented location-aware weather data retrieval pipelines using IMD forecasting systems and Mausamgram datasets.
  • Integrated local inference using Zephyr models and cloud-based inference using Gemma models through Hugging Face infrastructure.
  • Developed workflows for retrieving, processing, and contextualizing weather information before generating user-facing natural-language responses.
  • Improved system reliability by debugging, refactoring, and optimizing the existing RAG and inference pipeline architecture.
  • Enabled intelligent weather-query processing by combining real-time meteorological data retrieval with LLM-powered reasoning and response generation.

Links & Resources

Development & Product Projects

myCloud mySphere Aug 2025 - March 2026 Live

Distributed Personal Cloud Platform

Flask MySQL Socket.IO Python SDK CLI (mycloudctl) CustomTkinter Oracle OCI Nginx & Gunicorn
Cross-PlatformWeb, Desktop, Android, CLI, Python-SDK
Real-TimeSocket.IO Sync
DistributedStorage Nodes

Project Overview

myCloud is a full-stack, high-performance distributed personal cloud platform engineered for the mySphere ecosystem. It decouples file metadata from physical storage, allowing users to connect their own PCs and servers as distributed storage nodes ("Smart Relay") to build a self-hosted cloud with centralized control, real-time synchronization, enterprise-grade security, and multi-interface access.

Features

  • Multi-Client Access: Responsive Web PWA, CustomTkinter Desktop Node GUI, Python CLI (mycloudctl), official Python SDK (mycloud-sdk), and cross-platform Electron/Capacitor wrappers.
  • Distributed Storage Nodes: Connect private PCs or servers as storage nodes over WebSockets ("Smart Relay") with MySQL node routing.
  • Advanced Sharing Suite: Direct user invites with RBAC permissions, public token link generation, and batch share links grouping disparate files/folders.
  • Stash Lifecycle: Temporary file isolation workflow with scheduled auto-cleanup (10m, 1h, 1d, or custom intervals).
  • Authentication & Security: Google/GitHub OAuth, 2FA challenge flows, CSRF protection, HttpOnly/SameSite session security, and bcrypt hashing.
  • Observability & Monitoring: Built-in internal dashboards monitoring system CPU/RAM, Nginx/Gunicorn service health daemons, and product analytics.
  • Infrastructure & Hosting: Production-grade deployment on Oracle Cloud Infrastructure (OCI) using Nginx reverse proxy and Gunicorn Eventlet async worker processes.

Links & Resources

myCam mySphere VIT/College In Development

Real-Time WebRTC Security & Surveillance Platform

React 19 Vite 7 Express 5 Socket.IO WebRTC MySQL Tailwind CSS Canvas API AI Motion Detection Two-Way Audio
WebRTC P2PLow-Latency Stream
AI DetectionPerson/Vehicle/Pet
Two-Way AudioFull Duplex Talk-Back

Project Overview

myCam is a full-stack real-time video surveillance and security camera platform built with React 19, Express 5, Socket.IO, WebRTC, and MySQL. It turns smartphones, laptops, and webcams into live P2P broadcasting security feeds with AI object detection, custom canvas activity zones, two-way audio talk-back, scheduled recording, automated loop recycling, and direct myCloud storage integration.

Features

  • Phone as Security Camera: Transform any smartphone, laptop, or webcam into a live broadcasting IP camera with WebRTC P2P streaming and Google STUN NAT traversal.
  • AI Motion & Object Detection: Computer vision engine classifying target objects (Person, Vehicle, Pet) with Ray-Casting Point-in-Polygon zone intersection, bounding box overlays, and automated AI snapshot triggers.
  • Interactive Activity Zones: HTML5 Canvas overlay for drawing custom polygonal detection zones saved as normalized JSON coordinates to MySQL.
  • Two-Way Audio Talk-Back: Full duplex bidirectional WebRTC audio channel allowing talk-back communication from viewer dashboard to camera speakers.
  • Scheduled Recording: Automated schedule manager supporting time windows (HH:MM), day-of-week selection, camera targeting, and automated evaluation loops.
  • Storage Quotas & Loop Recycling: Multi-target storage (Internal, SD Card, Cloud) with custom quota limits and automated FIFO loop recycling to purge oldest clips when storage caps are reached.
  • Dynamic Quality & Bitrates: Live stream quality switching (4K 8Mbps, 1080p 3Mbps, SD 800kbps) with constraint-aware MediaRecorder encoding.
  • Smart Alerts & Web Push: Real-time Socket.IO alert relays, in-app toast alerts, and native browser Web Push desktop/mobile notifications.
  • myCloud Ecosystem Integration: Shared authentication pipeline with scrypt/pbkdf2 password hashing and direct video clip uploads to myCloud.

Links & Resources

mySync mySphere In Development

Cross-Device Notification & Clipboard Sync Platform

Node.js / Express Socket.IO WebRTC React Native React + Vite Groq / Llama 3 AI Clipboard Sync

myTune mySphere Jan 2026 - Feb 2026 Completed

Desktop Music Streaming Application

Python PyQt6 Media Streaming UI/UX Queue Management Desktop App API-Integration

Integrated Systems Projects

EMG Controlled Music System VIT/College Oct 2025 - March 2026 Completed

Bio-Signal Interface Project (Arduino)

Arduino Embedded Systems EMG Signals C/C++ DFPlayer Mini Ultrasonic Sensing OLED Interface

Other Projects

Shor's Algorithm Implementation VIT/College Feb 2026 - April 2026 Completed

Quantum Computing Project (Qiskit)

Qiskit Quantum Algorithms Python QFT Modular Exponentiation Classical Post-Processing Circuit Optimization Beauregard Architecture RSA Crypto Analysis Web Simulation (HTML/CSS)

Planned Projects

myMoney mySphere Planned

AI Expense Tracker & Budgeting Application

AI Financial Assistance Expense Tracking & Budgeting Investment Tracking SMS Parsing Cross-Platform Sync

Education

Vellore Institute of Technology

Bachelor of Technology - BTech, Computer Science | 2025 - 2029

Grade: CGPA: 9.44

  • Achieved a CGPA of 9.60 in Semester 1 and a cumulative CGPA of 9.44 after completion of the first year.
  • Secured 3rd rank in the branch during Semester 1 and maintained strong academic performance throughout the first year.
  • Coursework: C/C++, Python, R, MATLAB, Data Structures & Algorithms, Digital Electronics, Computer Architecture, SolidWorks, LTSpice.

Senior Secondary Education (Class 12th) | 2025

R.N. Public School, Prayagraj (CBSE Board)

Grade: 91.6%

  • Secured 91.6% overall in the Class 12th CBSE Board examinations.
  • Demonstrated academic excellence with strong and consistent performance in technical and analytical subjects.
  • Highlights: Scored 95/100 in Computer Science, 98/100 in English and 90/100 in Physics.

Senior Secondary Education (Class 11th) | 2024

R.N. Public School, Prayagraj (CBSE Board)

Grade: 91.1%

  • Secured 91.1% overall in the Class 11th CBSE examinations.
  • Demonstrated strong academic foundation in Computer Science and Mathematics.
  • Highlights: Scored 93/100 in Computer Science and 92/100 in Mathematics.

Secondary Education (Class 10th) | 2023

St. Joseph's College, Prayagraj (ICSE Board)

Grade: 96.8%

  • Secured 96.8% overall in the Class 10th ICSE Board examinations.
  • Achieved a perfect score of 100/100 in Computer Applications.
  • Scored 97/100 in English, 96/100 in Mathematics, 95/100 in Science, and 96/100 in History, Civics and Geography.

Certifications & Courses

Hyperspectral Data for Land and Coastal Systems | NASA ARSET

Techniques for utilizing hyperspectral datasets to monitor and assess land use and coastal environment changes.

View Certificate

Fundamentals of Remote Sensing | NASA ARSET

Core principles of remote sensing, including sensor types, data resolution, and interpreting satellite imagery.

View Certificate

Understanding climate data: From analysis of the past to future projections | ECMWF

Covered climate data analysis workflows, interpretation of historical datasets, and projection-focused methodology.

View Certificate

Understanding satellite data to monitor dust | EUMETSAT

Application of satellite data to monitor, analyze, and forecast dust patterns for atmospheric studies.

View Certificate

H SAF Products (Snow, Soil Moisture, Precipitation) | EUMETSAT

Comprehensive training on H SAF satellite products focusing on snow cover, soil moisture monitoring, and precipitation measurements for meteorological and hydrological applications.

View Snow Certificate View Soil Moisture Certificate View Precipitation Certificate

Honours & Rewards

Certificate of Merit - Branch Rank 3 | 2026

Vellore Institute of Technology (VIT), Chennai

  • Awarded for securing 3rd rank in the CSE branch.
  • Award was presented by Mr.Binod Kumar, MD & CEO of Indian Bank.
  • Award included a certificate.

Academic Excellence Scholarship | 2025

Indian Farmers Fertilizer Cooperative Limited (IFFCO)

  • Awarded by Indian Farmers Fertilizer Cooperative Limited (IFFCO) for securing 91.6% in the CBSE Class 12th Board Examination in 2025.
  • Award included a certificate, medal and a scholarship reward of Rs. 3,20,000

Academic Excellence Scholarship | 2023

Indian Farmers Fertilizer Cooperative Limited (IFFCO)

  • Awarded by Indian Farmers Fertilizer Cooperative Limited (IFFCO) for securing 96.8% in the ICSE Class 10th Board Examination in 2023.
  • Award included a certificate, medal and a scholarship reward of Rs. 24,000

Certificate of Excellence in Computer Applications | 2023

St. Joseph's College, Prayagraj

  • Awarded by St. Joseph's College for scoring a perfect 100/100 in Computer Applications in the ICSE Class 10th Board Examinations in 2023.
  • Award included a certificate and a trophy.

Featured in Amar Ujala for Board Results | 2023 & 2025

Amar Ujala Newspaper

  • Mentioned for securing 96.8% in Class 10th (ICSE).
  • Mentioned again for securing 91.6% in Class 12th (CBSE).
  • Recognized in merit-focused board result coverage for consistent academic achievement.

Featured in CollegeDekho VITEEE Toppers List | 2025

CollegeDekho.com

  • Recognized in the VITEEE toppers list feature.
  • Included in the published ranking highlights among notable VITEEE performers.

Skills & Technologies

Artificial Intelligence & ML

PyTorch, Deep Learning, Model Inference, Distributed Training, RAG, LLMs, Multi-LLM Consensus, Agentic AI, ChromaDB, GGUF Kernels, NLP

Backend & Systems Engineering

Python 3.10+, C++20, C, Java, Node.js / Express 5, Flask, REST APIs, WebSockets (Socket.IO), WebRTC P2P, llama.cpp Engine

Cloud, GPU & Infrastructure

HPC, Distributed Computing (NCCL, DDP), CUDA, ROCm, Vulkan, Metal, Oracle Cloud (OCI), Nginx, Gunicorn, systemd

Data & Storage Systems

SQL, MySQL, SQLite, ChromaDB Vector DB, GRIB/NetCDF, Geospatial GIS, Auto-regression

Frontend & App Development

React 19, React Native, Vite, CustomTkinter, HTML5, CSS3, JavaScript (ES6+), UI/UX, Electron, Capacitor, PyQt6, Canvas API

Security & Systems Architecture

OAuth 2.0, 2FA, System Design, Live Hysteresis VRAM Migration, Dynamic Microbatching, Distributed Storage

DevOps & Package Management

Git, GitHub, PyPI Publishing (mycloudctl, mycloud-sdk, inferenceos), CI/CD, Docker, Linux Administration

Hardware & Embedded Systems

Arduino C++, Sensors, Bio-signals (EMG), Ultrasonic Sensing, DFPlayer Mini, OLED Displays, Serial Protocols

Languages

English (Fluent), Hindi (Native), German (Elementary)

Contact/Socials

Phone : +91 8429632553

GitHub : github.com/rudy-07

Website : mysphere.co.in

LinkedIn : linkedin.com/in/rudransh-shekhar/

Email : kanhaiffco2007@gmail.com

Resume & Profile

Rudransh Shekhar - Resume

Comprehensive overview of my professional experience, projects, technical skills, and academic background.

View Full Resume

9.44

B.Tech CGPA

1+

Years Exp

10+

Projects

3rd

Branch Rank

AI/ML

Core Focus

5+

Certifications

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