Passionate AI Engineer | Deep Learning Researcher | Backend Developer

Technical Skills: Python, Machine Learning, Deep Learning, TensorFlow, Hugging Face, Flask, Django, SQL, DVC, MlFlow, Pandas, NumPy, Computer Vision, NLP

Education

• B.Sc. in Computer Science & Engineering
  American International University-Bangladesh (Jan 2020 - Dec 2023)
  • Recipient of the Dean’s Award for remarkable academic achievement
• H.S.C. in Science
  BAF Shaheen College, Dhaka (Jun 2018)

Experience

AI Engineer-TechnyX.ai (Full time On-site) (December 2024 - Present)

• Led development of advanced computer vision and multimodal AI solutions, address and solved real-world industry challenges with 98% accuracy.
• Architected and deployed vision-language models for in-house API development, optimizing inference cost by 40% and reducing computational costs through efficient model deployment.
• Engineered state-of-the-art computer vision algorithms and end-to-end AI applications, integrating LLMs with computer vision systems for building robust solution.
• Collaborated with cross-functional teams to design and deploy scalable AI software solutions, ensuring seamless integration of multiple AI technologies across the platform

Freelancer-UpWork (Part-time Remote) (October 2024 - Present)

• Developed a state-of-the-art super-resolution model for image upscaling, leveraging advanced techniques to enhance image quality and detail.
• Modified the architecture of the ”Dual Attention Transformer” to effectively upscale images from two distinct sources, ensuring optimal performance and quality.
• Designed a robust architecture that utilizes channel-wise concatenation of images, focusing on the most efficient features to improve the overall upscaling process.
• Conducted extensive testing and validation of the model to ensure high accuracy and reliability in various real-world applications.
• Collaborated with clients to understand their specific needs and provided tailored solutions, resulting in a 30% increase in client satisfaction ratings.

Junior AI Developer CRTVAI (Full-time Remote) (April 2024 - September 2024)

• Designed and implemented the system architecture and API endpoints of an automated quality control AI application to enhance customer care services.
• Fine-tuned speech-to-text models, deploying server-less models to minimize third-party transcription costs.
• Improved the Voice Activity Detection pipeline and conducted a detailed analysis of speech metrics to enhance accuracy and efficiency.
• Achieved a 25% reduction in GPT API calling costs through strategic optimization.
• Implemented Background task processing techniques to significantly enhance system performance and reliability.

Researh Assistant in Computer Vision (Full-time) (Sep 2023 - Jan 2024)

• Collaborated with the research team to contribute to Computer Vision research project.
• Conducted comprehensive research, experiments, and data analysis to support project objectives.
• Research Collaboration: Actively participated in a Computer Vision research project, leveraging my skills and knowledge to contribute to the team’s success.
• Research Dataset: Contributed to the creation of a research dataset containing 12,000+ labeled images, supporting future computer vision research and applications.
• Research and Data Analysis: Reduced research time by 20% by implementing efficient data analysis and documentation practices.
• Documentation & Presentation: Successfully presented research progress and insights to the research group and advisor, ensuring clear communication and project transparency.

Research Experience

1. [Draft] Research: A Large-Scale Action Dataset

Publications

1. [Under-Review (Scientific Reports)] Research: High-Accuracy Image Segmentation for Self-Driving Cars
2. Speech Emotion Recognition using Transfer Learning Approach and Real-Time Evaluation in English and Bengali Language ResearchGate

Projects

(Chat Bot) MediChat-Assistant

• Developed a medical chatbot application leveraging the open-source LLM Llama-2 7B-chat with Retrieval-Augmented Generation (RAG) for accurate, source-based responses.
  • Accurate medical assistance: Utilized RAG to provide precise, context-aware responses by retrieving relevant information from trusted medical sources.
  • User-friendly interaction: Built a seamless interface using FastAPI, enabling easy access to the chatbot via RESTful APIs.
  • Enhanced response quality: Integrated Langchain for efficient prompt engineering and improved conversational flow.
  • Scalable and modular: Designed with a modular architecture, allowing for easy integration of additional features or datasets.
• Tech: LLM (Llama-2 7B-chat), RAG, VectorDB, Python, FastAPI, Langchain
• Skills: Natural Language Processing, API Development, Prompt Engineering, Vector Database Integration

(Python Package) SpectraClassify

• Built a user-friendly web application for training custom image classification models without writing code.
  • Effortlessly create custom models: Train models on their own data with a simple interface, eliminating the need for coding expertise.
  • Experiment with diverse models: Select from a variety of pre-trained models or fine-tune existing ones for specific tasks.
  • Seamless testing and inference: Upload images or utilize webcam input to test the accuracy and effectiveness of their trained models.
  • Published as a PyPI package: Enabling broader accessibility and facilitating quick image classification model training with minimal code for developers.
• Tech: Python, TensorFlow, JS, HTML, Bash
• Skills: Deep Learning, Web Development, CICD pipeline.

Kidney Tumors and Stones Classification

• A deep learning project aimed at accurately classifying kidney tumors and stones from medical images.
  • The project is designed to integrate state-of-the-art machine learning techniques with robust data management and collaborative tools, providing a significant contribution to medical imaging analysis.
• Tech: Python · TensorFlow · DVC · MLOps.
• Skills: Deep Learning, Web-Development, Version Control.

Community Platform for AIUB Students - AIG

• Developed a comprehensive web application (ASP.NET Web API) using C# and Entity Framework to foster the AIUB student community. Adhering to SOLID principles and a robust 3-tier architecture, the platform empowers students with:
  • Engaging communication: Features for students to connect and share information.
  • Streamlined resume building: Efficient tools for creating resumes for the users.
  • Job opportunities: Centralized platform for posting and applying to job openings with one-click ease.
  • Advanced access control: Admin control over post and job moderation, user management, and security.

• Responsibilities included: Full database design, Use-case analysis, Authentication & authorization, Development of features and API endpoints

• Tech:ASP.NET, C#, Entity-Framework, Git
• Skills: Software Development, Version-controll

Emotion Classification from Face Images in Real-Time

• Developed a real-time emotion classification system using a custom deep learning model and Flask Web API. This project enables users to:
  • Classify emotions in real-time: Leverage their webcam to capture facial expressions and receive instant emotion classification through a user-friendly web interface.
  • High-accuracy classification: The custom deep learning model delivers accurate emotion identification, fostering potential applications in various fields.
• Tech: Python,TensorFlow, OpenCV, Flask, JavaScript, HTML, CSS.
• Skill: Deep Learning, Image processing, Web-development.

On-the-Go-Podcast

• Developed a podcast app using PHP to empower podcast enthusiasts. This platform offers:
  • Streamlined organization: Efficient tools for managing playlists, and listening progress.
  • Seamless podcast experience: Effortlessly discover, subscribe to, and enjoy favorite podcasts, all within a user-friendly interface.
• Tech: PHP, JavaScript, Bootstrap, CSS, HTML, Git
• Skills: Web development

Fashion Recommendation System Using ResNet-50

• An end-to-end fashion recomendation system uisng deep learning and machine learning.
  • Feature extraction using Resnet50
  • Similarity matching using k nearest neighbours (KNN)
• Tech: Python, Numpy, Tensorflow, Scikit-learn, Git
• Skills: Machine learning

Chrome Dingo Clone Build with OpenGL

• A 2D Chrome Dino-like game using OpenGL(freeglut) and C++

• Tech: C++, OpenGL
• Skills: Computer Graphics, Game logic

Hospital Management System

• Developed a comprehensive hospital management system to streamline operations and enhance efficiency in multi-department healthcare facilities. This desktop application features:
  • Multi-user access control: Granular role-based permissions for doctors, nurses, administrators, and other staff members.
  • Comprehensive department management: Dedicated modules for various departments like admissions, billing, pharmacy, and patient records.
  • User-friendly interface: Intuitive design for seamless navigation and efficient data management.
• Tech: C#, C#-Form , MS-sql, Git
• Skill: Desktop application, Database design.

  Honors and Awards

• Dean’s List Award - for outstanding academic performance in undergrad.

Contract

• Reach me at email: sadhin.aiub.cse@gmail.com
• Follow me on Linkedin