Md Ashfaq Salehin, Developer in London, United Kingdom
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Md Ashfaq Salehin

Verified Expert  in Engineering

Full-stack Developer

London, United Kingdom

Toptal member since October 31, 2018

Bio

Ashfaq is an AI researcher and full-stack software engineer with expertise in Python, Java, Kotlin, and JavaScript. He is pursuing a PhD in artificial intelligence at the University of Sussex, and his research focuses on temporal graph neural networks. With over eight years of experience, including roles at Meta and HelloFresh, Ashfaq combines academic excellence and engineering expertise to deliver innovative AI and software solutions.

Portfolio

University of Sussex
Artificial Intelligence (AI), Deep Learning, Networks...
Local Staffing LLC
Node.js, React, Elasticsearch, MongoDB, TensorFlow, Amazon SageMaker, Python...
Meta
Android, Kotlin, Java, HTML, Docker, JSON, Microservices, APIs, Git...

Experience

  • Node.js - 4 years
  • Python - 4 years
  • Elasticsearch - 3 years
  • PyTorch - 3 years
  • Amazon Web Services (AWS) - 3 years
  • Deep Learning - 2 years
  • TensorFlow - 2 years
  • Natural Language Processing (NLP) - 1 year

Availability

Part-time

Preferred Environment

Slack

The most amazing...

...project I've worked on is a workload prediction model of a large-scale microservice network using a cutting-edge, dynamic network embedding technique.

Work Experience

PhD Researcher (AI)

2024 - PRESENT
University of Sussex
  • Awarded Sussex AI PhD studentship from the School of Engineering and Informatics, University of Sussex, to fully fund my PhD.
  • Conducted pioneering research in temporal graph neural networks for dynamic systems modeling.
  • Developed scalable libraries for large-scale temporal network analysis.
  • Worked as a teaching assistant for various academic modules, including Computer Networks and Operating Systems, for MSc students.
Technologies: Artificial Intelligence (AI), Deep Learning, Networks, Deep Neural Networks (DNNs), Data Science, Scientific Computing, Scientific Data Analysis, PyTorch, Network Science, IP Networks

Senior Software Engineer

2023 - 2024
Local Staffing LLC
  • Developed a large XML feed builder application using Node.js, Elasticsearch, and React.
  • Created a sentiment classification system using NLP, TensorFlow, and Amazon SageMaker.
  • Engineered a job recommendation system using NLP, TensorFlow, and Amazon SageMaker.
  • Wrote serverless Lambda functions to deploy specific functionalities.
Technologies: Node.js, React, Elasticsearch, MongoDB, TensorFlow, Amazon SageMaker, Python, Deep Learning, Natural Language Processing (NLP), Classification, CSS, HTML, BigQuery, Data Engineering, Docker, Docker Compose, JSON, TypeScript, Full-stack, Amazon Web Services (AWS), Microservices, APIs, Git, REST APIs, Relational Databases, AWS Lambda, Amazon S3 (AWS S3), MapReduce, Object-relational Mapping (ORM), REST, Back-end Development, Front-end Development, CI/CD Pipelines, Apache Kafka, Software Engineering, NoSQL, Django

Software Engineer (Android)

2022 - 2023
Meta
  • Solved cross-organization problems in Facebook, WhatsApp, and Instagram applications.
  • Maintained relationships with stakeholders in Meta's partner companies, such as Google and Samsung.
  • Integrated and maintained third-party service implementations inside Facebook and WhatsApp.
  • Oversaw the development and improvement of features inside Facebook and Whatsapp applications using Kotlin.
Technologies: Android, Kotlin, Java, HTML, Docker, JSON, Microservices, APIs, Git, Front-end Development, Software Engineering

Kotlin/Android Developer

2021 - 2022
HelloFresh USA
  • Designed and implemented multiple important features from scratch in a HelloFresh Android application using Kotlin.
  • Helped in the transition of architecture from MVP to MVI.
  • Wrote mocked UI tests using the Espresso framework and Kotlin.
  • Integrated internal and external backend APIs using Retrofit and Kotlin.
  • Participated in internal product design and scrum meetings.
  • Analyzed requirements and created Jira tasks and subtasks. Estimated efforts in the implementation of complex features.
  • Performed code review and hosted meetings in case of complex issues.
  • Conducted release testing for the teams to which I was assigned.
Technologies: Android, Android SDK, Unit Testing, UI Testing, Continuous Integration (CI), JSON, APIs, Mobile Development, Git, Integration Testing, REST, Front-end Development, Jetpack Compose, Android Jetpack, Software Engineering

Java/Kotlin Developer (Android)

2020 - 2021
OPN
  • Led, designed, and developed various Android applications and internal SDKs.
  • Developed a major part of the Toyota Wallet application using React Native and later Kotlin.
  • Designed and developed internal SDKs for the company, such as a storage framework and user kit using Kotlin.
  • Integrated in-house and third-party APIs using Kotlin.
  • Contributed to developing back-end APIs using the Kotlin language and the Spring Boot framework.
Technologies: Kotlin, Android SDK, RxJava, Dagger 2, Hilt, JUnit, React Native, JSON, Microservices, APIs, Mobile Development, Git, Integration Testing, REST, Front-end Development, Android Jetpack, Software Engineering

Senior Software Engineer

2019 - 2020
Agoda
  • Worked on the flight post-booking system developed using Scala and the Spring Boot framework.
  • Developed additional back-end APIs using Java and the Spring Boot framework.
  • Developed front-end application features using JavaScript and React.
  • Built log analytic systems using Scala, Spark, ELK stack, and Spring Boot frameworks.
Technologies: React, Java, Scala, Hadoop, Spark, GraphQL, Jest, CSS, HTML, Data Engineering, Docker, Docker Compose, JSON, TypeScript, PostgreSQL, Full-stack, Microservices, APIs, Git, REST APIs, Relational Databases, Spring, MapReduce, Hibernate, Java Persistence Query Language (JPQL), SQL, Object-relational Mapping (ORM), Integration Testing, REST, Spring Boot, Back-end Development, Front-end Development, CI/CD Pipelines, Kafka Streams, Apache Kafka, Software Engineering, NoSQL

System Developer

2018 - 2019
DIAKRIT International
  • Designed and developed various features in the company's order management system, built with Python and the Django framework.
  • Developed several REST APIs using the Laravel framework.
  • Architected the front-end shop website built with Vue.js and React.
  • Managed deployed services on the AWS cloud platform.
  • Identified bugs, created bug tickets, and communicated with other teams to help with prioritization.
Technologies: Vue, JavaScript, React, CSS, HTML, Docker, JSON, TypeScript, Full-stack, Amazon Web Services (AWS), Microservices, APIs, Git, REST APIs, Relational Databases, Hibernate, Object-relational Mapping (ORM), SQL, MySQL, REST, Back-end Development, Front-end Development, CI/CD Pipelines, Apache Kafka, Software Engineering, Python, Django, Laravel

Senior Full-stack Developer

2017 - 2018
VinAudit.com
  • Worked with a price summarization system from millions of car sale records using Spark and MySQL.
  • Built widgets to display car price data aggregated and summarized using Spark.
  • Created various integration scripts for client companies using our services.
  • Developed custom data feeds for important clients.
Technologies: JavaScript, PHP, CSS, HTML, Docker, JSON, PostgreSQL, Full-stack, Amazon Web Services (AWS), Microservices, APIs, Git, REST APIs, Relational Databases, SQL, MySQL, Back-end Development, Front-end Development, Software Engineering

Experience

Workload Prediction in Microservice Networks

https://github.com/ashfaq1701/temporal_gnn_network_log_data
I developed a novel approach that combines temporal graph neural networks (TGN) with transformer-based forecasting for microservice workload prediction. Leveraging Alibaba's 2022 microservice dataset, I achieved improved accuracy compared to existing methods, especially under peak traffic conditions. I designed temporal embeddings to capture dynamic service interactions, demonstrating enhanced long-term prediction capabilities.

Temporal Walk: Dynamic Network Sampling Library

https://github.com/ashfaq1701/temporal_walk
I developed Temporal Walk, a high-performance library for constructing and sampling temporal networks to enable efficient training of graph neural networks (GNNs). The library is designed for scenarios where graph structures evolve over time, such as social networks, communication systems, and financial transaction graphs.

My key contributions include implementing efficient algorithms in C++ for temporal walk sampling and incorporating various biasing strategies such as uniform, linear, and exponential. I supported dynamic graph pruning with a sliding time window to optimize memory usage in long-running systems. Additionally, I exposed functionality through Python bindings using PyBind11, enabling seamless integration into data science workflows. I also focused on optimizing scalability, ensuring the system could process large-scale temporal graphs in real time.

This project plays a critical role in applications like dynamic node embedding, anomaly detection, and temporal link prediction. My responsibilities encompassed the full development lifecycle, including architecture design, implementation, performance tuning, and API integration.

Transformer-based Learning-to-rank Model for Listing Price Optimization

I developed a transformer-based learning-to-rank (LTR) model to optimize listing ranking, focusing on price adjustments to improve visibility and user engagement.

My key responsibilities included preprocessing large-scale scraped listing rank data and implementing an LTR pipeline in PyTorch using pairwise hinge loss and ranking accuracy. I evaluated model performance using pairwise hinge loss and MRR metrics, ensuring a comprehensive assessment of the model's effectiveness. Additionally, I focused on optimizing the model for scalable, low-latency deployment to ensure efficient performance in production.

The project successfully improved listing ranking and click-through rates, showcasing the effectiveness of deep LTR models in large-scale platforms.

Learning to Play Atari Games Using Dueling Q-learning and Hebbian Plasticity

https://arxiv.org/pdf/2405.13960
I developed an advanced reinforcement learning agent using Dueling Double DQN and neuroplasticity-inspired models. By integrating Hebbian plasticity, I enabled lifelong learning and compared the agent's performance against traditional methods. I advanced the field of adaptive systems by implementing plastic neural networks and testing them in the challenging domain of Atari games.

Mixi | Audio Editor, Recorder, and Mixer

This personal project is an Android application for recording, editing, and mixing audio files. A limited open-source version of this application is available here: Github.com/ashfaq1701/fast-mixer.

Project Highlights:
• This is an NDK-based project. Most of the operational parts of the project are done as native codes.
• This project is built with Android architecture components, MVVM, LiveData, navigation, data binding, coroutines, and more.
• Hilt is used as a DI framework.
• RxJava and a reactive programming pattern are used to act against various view events.
• Used Google Oboe as the audio streaming framework and FFmpeg for decoding and encoding audio.

Toyota Wallet (Thailand)

I developed this application as part of my job responsibilities while working at OPN. I built many core features of the application with native Kotlin MVVM architecture. and, as part of my job role, I owned some important features of this application.

Education

2023 - 2024

Master of Science Degree in Artificial Intelligence and Adaptive Systems

University of Sussex - Brighton, East Sussex, United Kingdom

2009 - 2013

Bachelor of Science Degree in Electronics and Communication Engineering

Khulna University - Khulna, Bangladesh

Certifications

JUNE 2024 - PRESENT

International English Language Testing System (IELTS)

British Council and Cambridge ESOL

MAY 2023 - MAY 2026

TensorFlow Developer Certificate

Tensorflow

APRIL 2021 - APRIL 2024

Associate Android Developer

Google Developers

APRIL 2020 - APRIL 2022

CCA Cloudera Spark and Hadoop Developer

Cloudera

NOVEMBER 2012 - NOVEMBER 2015

Cisco Certified Network Associate

Cisco System INC

Skills

Libraries/APIs

React, RxJava, REST APIs, TensorFlow, PyTorch, Vue, Node.js, Scikit-learn

Tools

Android NDK, Git, Docker Compose, Android Jetpack, ChatGPT, Amazon SageMaker, BigQuery, Kafka Streams, PyCharm

Languages

Kotlin, JavaScript, Java, PHP, Python, Scala, C++, TypeScript, Java Persistence Query Language (JPQL), SQL, C, CSS, HTML, GraphQL, Rust

Frameworks

Android SDK, React Native, Spring Boot, Laravel, Hadoop, Spark, Dagger 2, Hibernate, Jest, Spring, JUnit, Django

Paradigms

REST, Microservices, Mobile Development, Unit Testing, Continuous Integration (CI), Object-relational Mapping (ORM), MapReduce

Platforms

Android, Docker, Apache Kafka, Amazon Web Services (AWS), AWS Lambda

Storage

MySQL, PostgreSQL, Relational Databases, Elasticsearch, JSON, NoSQL, MongoDB, Amazon S3 (AWS S3)

Other

Hilt, Full-stack, UI Testing, Integration Testing, English, Deep Learning, Machine Learning, Data Engineering, Back-end Development, Jetpack Compose, CI/CD Pipelines, Software Engineering, Audio Streaming, Audio, Computer Vision, Natural Language Processing (NLP), Image Processing, Front-end Development, IP Networks, VoIP, APIs, Computer Networking, Classification, Regression, Time Series Analysis, Artificial Intelligence (AI), Neural Networks, Network Science, Large Data Sets, Reinforcement Learning, Adaptive Control Systems, GPU Computing, Networks, Deep Neural Networks (DNNs), Data Science, Scientific Computing, Scientific Data Analysis, Scraping, Large Language Models (LLMs)

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