Raju Sah, Best Java Dev in KTM | Full Stack Java Developer

Java framework for building REST APIs and backend applications with dependency injection, configuration, and database integration.

Python framework for building APIs with type-based validation, asynchronous request handling, and generated API documentation.

React framework for building web applications with routing, server rendering, and integrated server-side functionality.

PHP framework for building web applications with routing, authentication, database models, queues, and reusable application structure.

Interactive Python interfaces for presenting and testing machine learning models.

Python web framework for building database-backed applications, with an ORM, routing, and an integrated admin interface.

Node.js web framework for HTTP routing, middleware, and API development.

TypeScript framework for structured backend applications using modules, controllers, and dependency injection.

JavaScript library for building interactive interfaces with reusable components, state management, and predictable data flow.

TypeScript framework for building web applications with components, dependency injection, reactive forms, and routing.

JavaScript framework for building reactive interfaces with reusable components and flexible application structure.

Semantic page structure for portfolio interfaces and interactive research demos.

Responsive layouts, styling, and presentation for web interfaces and research dashboards.

Embedded relational database for lightweight applications, local storage, and SQL-based data management without a separate database server.

Relational database for structured application data, SQL queries, transactions, indexing, and reliable data integrity.

Relational database for storing and querying application data with SQL, transactions, indexes, and table relationships.

Document database for storing flexible JSON-like records and working with queries, indexes, and aggregation pipelines.

In-memory data store for caching, session storage, queues, and fast access to frequently used application data.

Deep learning framework for building and training neural networks with tensors, automatic differentiation, and GPU acceleration.

Machine learning framework for building, training, and deploying models for tasks such as image classification and prediction.

High-level deep learning API for defining neural networks, training models, and evaluating experiments with reusable layers.

Python machine learning library for preprocessing data, training classical models, tuning parameters, and evaluating predictions.

Convolutional neural networks for learning spatial features from images in classification, detection, and segmentation tasks.

Gradient-boosted decision trees for tabular classification, including the airline satisfaction competition baseline.

Gradient boosting with categorical-feature support for tabular machine learning experiments.

Gradient-boosted tree models for classification, validation, and ensemble experiments.

Computer vision library for processing images and video, extracting visual features, and building real-time vision workflows.

Optical character recognition engine for extracting machine-readable text from images and scanned documents.

Medical imaging tools for volumetric preprocessing and planned 3D segmentation experiments.

Python image loading, conversion, resizing, and preprocessing utilities.

Image-processing utilities for computer vision preprocessing and analysis.

Lightweight model conversion and deployment, including FP16 model optimization.

CPU inference for exported computer vision models, including face detection and recognition.

AI approach that retrieves relevant information from a knowledge source and supplies it as context for generated responses.

Text cleaning, tokenization, and linguistic preprocessing for classification pipelines.

Word2Vec embeddings and text representation for natural language processing experiments.

Pretrained transformer models and fine-tuning workflows for text classification, including DistilBERT.

Python library for cleaning, transforming, joining, and analyzing tabular data with DataFrames.

Python library for numerical computing with multidimensional arrays, vectorized operations, and linear algebra.

Scientific computing, statistical analysis, and signal-processing utilities for reproducible Python research.

Columnar data processing and Parquet storage for machine learning datasets and experiment pipelines.

Interactive notebooks for data exploration, model experiments, and research analysis.

Container platform for packaging applications and dependencies into reproducible environments for development and deployment.

Automated workflows for building, checking, testing, and delivering application changes consistently.

Version control for application development, research code, and reproducible experiment configuration.

Python dependency locking and reproducible environments for research and competition projects.

Command-line access to competition datasets, kernels, submission validation workflows, and leaderboard results.

Command-line management of Google Colab runtimes for remote Python execution, GPU/TPU sessions, notebook logs, and model or dataset artifact retrieval.

Browser automation framework for testing web application flows, interactions, and behavior across browser engines.

Automated Python tests for data-processing, model, and evaluation behavior.

Standard-library Python tests for reproducible research pipelines and numerical utilities.

Python plotting library for creating charts, inspecting data, and visualizing model results.

Statistical visualizations for exploratory analysis and machine learning evaluation.

Framework for building Android and iOS applications with React components and shared JavaScript or TypeScript code.