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AI Engineer with less than a year in Deep Learning & Multi-agent Systems
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Akib Uddin Nayan is a proactive AI Engineer specializing in Deep Learning, Natural Language Processing, and Computer Vision. With a strong academic background in CSE and relevant certifications, Akib has built several multi-agent systems and machine learning models. His expertise includes LangChain, LangGraph, Groq API, and various deep learning frameworks, demonstrating a solid foundation in developing intelligent solutions.
Port City International University
Master of Science - CSE · Computer Vision, Artificial Intelligence, Human-Computer Interaction, Natural Language Processing, Computer Graphics, Animation
August 1, 2024 – June 30, 2025
BGC Trust University Bangladesh
Bachelor of Science - CSE · Data Structure and Algorithm, Artificial Intelligence, Computer Graphics, Fuzzy Logic and Neural Networks
August 1, 2019 – June 30, 2022
Customer Support with Handoffs
June 24, 2026 – Present
Build a Customer Support Agent that implements the state machine pattern and does the following things: Collects warranty information before proceeding. Classifies issues as hardware or software. Provide solutions or escalations to human support. Maintain conversation state across multiple turns.
View ProjectSales and Support with Handoffs
June 24, 2026 – Present
Build a Multi-Agent System with separate Sales and Support agents. Where each agent is a separate graph node, and handoff tools allow agents to transfer the conversation to one another.
View ProjectFake News Classification
June 24, 2026 – Present
Tools-> TFidfVectorizer, WordCloud, word_tokenize, stopwords, WordNetLemmatizer. Algorithm-> MultinomialNB, BernoulliNB, Logistic Regression. Created a pipeline for the whole process. Higher accuracy in logistic regression was around 95%.
View ProjectCustomer Support Automation Agent
June 24, 2026 – Present
Build a multi-agent system where the customer sends the query, and the agent performs sentiment analysis for this query, and finally provides a quality score of the customer support response.
View ProjectPersonal Assistant with Subagents
June 24, 2026 – Present
Build a Personal Assistant System that coordinates two specialists with fundamentally different responsibilities. A calendar agent that handles scheduling, availability checking, and event management. An email agent that manages communication, drafts messages, and sends notifications. I also incorporate human-in-the-loop review, allowing users to approve, edit, or reject actions as desired.
View ProjectText Summarization
June 24, 2026 – Present
Project Overview: Tech stack: Python, PyTorch, Hugging Face, NLP libraries. Algorithms: Google/Pegasus-cnn_dailymail. Core contribution: Model training, preprocessing, and evaluation. Evaluation: Rouge Score, real-world usability. Implementation: Web Framework (FastAPI), Modular Code.
View ProjectDeep Learning and Generative AI
AiQuest
December 1, 2024 – August 1, 2025
Data Science and Machine Learning with Python
AiQuest
May 1, 2024 – September 1, 2024
Web Scraping With Python
Data Nerd
January 1, 2024 – March 1, 2024
Python for Data Science Level 1
Grad Bunker Learning Hub
September 1, 2023 – December 1, 2023
Data Analyst Bootcamp
Data Solution 360
May 1, 2023 – August 1, 2023
Cultural Fit Analysis
The candidate's project portfolio shows a strong interest in AI and machine learning applications, particularly in conversational AI and agent systems. This aligns well with an AI Engineer role. The diversity of projects (customer support, personal assistant, text summarization, fake news classification) indicates a broad curiosity and willingness to explore different problem domains within AI. The certifications further reinforce a proactive learning attitude. However, the lack of team-based projects or open-source contributions makes it difficult to assess collaboration and cultural integration in a professional team setting.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to break down complex problems into manageable components (e.g., subagents, state machine patterns). The inclusion of 'human-in-the-loop review' in a personal project suggests an understanding of practical system deployment and user interaction, which is a positive for operational fit. However, without direct work experience, it's difficult to assess collaboration, stress handling, or communication in a professional setting.