• Software engineer
• Embedded System Engineer
• System Design Engineer
• Data Engineer
• ML Engineer

Position: Software & Solution Engineer
Department: Software Research & Engineering Department
Division: Product & Technology Division

Position: Independent Contractor
Project: Emergency Response for COVID-19 Homegrown Ventilator

Position: Independent Contractor
Project: Online Report Delivery Management System for Armed Forces Institute of Pathology (AFIP)

• bKash Customer App
• bKash Agent App
• Street Stat
• Weather App
• Federated Extra Tree (Loan Approval Classifier)
• Bangla OCR
• Dume / Dume Web

• Flutter
• Android
• iOS
• Spring Boot
• Node.js
• Flask
• Django
• .net Framework
• Rails
• React
• Next.js
• NGINX
• Docker
• Kubernetes
• Keras & Tensorflow
• Jenkins
• Hadoop Ecosystem (Big Data)
• Apache Airflow

• Redis
• Oracle
• PostgreSQL
• Firestore
• Apache Hive
• MongoDB

• AWS
  • EC2
  • S3
• Azure
  • Azure Web App
  • Azure ML Studio
  • Storage account
  • Azure Synapse analysis
• Google Cloud Platform(GCP)
  • Compute Engine
  • Cloud Storage
  • Cloud Functions
  • ML-Kit
  • Firebase
  • Google Map API

• RESTful API
• GraphQL
• gRPC
• WebSocket
• Message Passing Interface(MPI)
• Webhook
• MQTT
• Kafka

• Android Studio
• IntelliJ IDEA
• PyCharm
• WebStorm
• GoLand
• RubyMine
• Jupyter Lab
• Adobe XD
• Xcode

• Business Logic Component (BLoC)
• Clean Architecture
• Layered Architecture
  • Data Layer
  • Domain Layer
  • Presentation Layer
• Microservices Architecture
• Reactive Architecture
• Repository Pattern
• Model-View-ViewModel (MVVM)
• Model-View-Presenter (MVP)
• Model-View-Controller (MVC)
• Builder Pattern
• Factory Pattern

• Object Oriented Programming
• Aspect Oriented Programming
• Functional Programming
• Reactive Programming
• Concurrent Programming

• Java
• Kotlin
• C
• C#
• Dart
• JavaScript
• TypeScript
• Python
• Bash
• JSON Schema
• Golang
• Ruby

• Graph Theory
• Parallel Algorithms
• Advanced Databases
• Symbolic Machine Learning 1
• Symbolic Machine Learning 2
• Neural Networks & Fuzzy Systems

In Graph Theory, I learned about the study of graphs and their properties. I gained knowledge about various types of graphs such as directed graphs, undirected graphs, weighted graphs, and so on. I also learned about the applications of graphs in various fields such as computer science, mathematics, and social sciences.

Parallel Algorithms course introduced me to the techniques for designing and analyzing algorithms that can run on parallel computing platforms. I learned about the different architectures of parallel computers and the models used for their analysis. I also gained knowledge about the performance analysis of parallel algorithms.

Advanced Databases course focused on the advanced concepts and techniques used in database design and management. I learned about the various types of database models such as relational, object-oriented, and XML. I also gained knowledge about advanced database technologies such as distributed databases, data warehousing, and data mining.

Symbolic Machine Learning 1 and 2 courses provided me with a comprehensive understanding of symbolic machine learning. I learned about the different approaches to machine learning such as inductive logic programming, decision tree learning, and rule induction. I also gained knowledge about the application of machine learning in various fields such as natural language processing and robotics.

The Neural Networks & Fuzzy Systems course introduced me to the concepts of neural networks and fuzzy logic. I learned about the different types of neural networks such as feedforward networks, recurrent networks, and self-organizing maps. I also gained knowledge about fuzzy logic and its applications in decision-making and control systems.

• Deep Learning with Python, Second Edition
• Reinforcement Learning: An Introduction
• Speech and Language Processing: Third Edition

Deep Learning with Python, Second Edition is a book written by François Chollet, the creator of the Keras deep learning library. The book provides a comprehensive introduction to deep learning and covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. The book also includes practical examples of deep learning applications in computer vision, natural language processing, and generative models.

Reinforcement Learning: An Introduction is a book written by Richard S. Sutton and Andrew G. Barto. The book provides a comprehensive introduction to reinforcement learning, which is a subfield of machine learning that deals with learning through trial-and-error interactions with an environment. The book covers topics such as Markov decision processes, dynamic programming, Monte Carlo methods, and temporal-difference learning. The book also includes practical examples of reinforcement learning applications in robotics, game playing, and control systems.

Speech and Language Processing: Third Edition is a book written by Dan Jurafsky and James H. Martin. The book provides a comprehensive introduction to natural language processing, which is a subfield of artificial intelligence that deals with the interaction between computers and human language. The book covers topics such as language modeling, part-of-speech tagging, syntactic parsing, and sentiment analysis. The book also includes practical examples of natural language processing applications in speech recognition, machine translation, and information retrieval.

• Aviation and avionics
• Astronomy and space
• Competitive programming
• Artificial General Intelligence/AGI/Singularity
• Piano/Keyboard Playing
• Aeroponic, Aquaponic, & Hydroponic Vertical Farming
• Gourmet traveling & ecotourism