Md. Mubasshir Naib
> |_
“Strongly determined about career goal. Highly passionate about learning newer things daily.”
Md. Mubasshir Naib
Software Engineer I
About
Built for intelligent systems.
I connect applied AI, product engineering, and research practice to build useful automation systems with clear business value.
I am an AI Integration & LLM Engineer at Brain Station 23 with a strong background in Natural Language Processing, Large Language Models, and full-stack development. I graduated with a B.Sc. in Computer Science and Engineering from CUET with a CGPA of 3.79 (with Honors).
My work focuses on building enterprise-grade AI agents, RAG pipelines, and intelligent automation solutions. I am passionate about solving complex problems through AI and have published research at NAACL and LT-EDI workshops.
Education
B.Sc. in Computer Science and Engineering
Chittagong University of Engineering and Technology
2020 – 2025 · 3.79 with Honors
Higher Secondary Certificate (H.S.C.)
Dhaka Residential Model College, Dhaka
2019 · GPA 5.00
Secondary School Certificate (S.S.C.)
Dhaka Residential Model College, Dhaka
2017 · GPA 5.00
Thesis
Multi-label Emotion Classification on Code-Mixed Text (Banglish)
Developed and evaluated ML, DL, Transformer, and LLM-based models for multi-label emotion classification on Bangla-English code-mixed text across 9 emotion categories.
Best result achieved 78.64% F1-score using Mistral-7B; paper submitted to IEEE SPICSCON 2026 (Under Review).
Supervisor: Prof. Dr. Mohammad Shamsul Arefin
Research
Research Experience
Natural language processing research focused on low-resource and code-mixed languages, emotion classification, and social media content moderation.
Undergraduate Thesis Researcher
Chittagong University of Engineering and Technology
Multi-label Emotion Classification on Code-Mixed Text
- Developed and evaluated ML, DL, Transformer, and LLM-based models for multi-label emotion classification on Bangla-English code-mixed text across 9 emotion categories.
- Achieved 78.64% F1-score using Mistral-7B; paper submitted to IEEE SPICSCON 2026 (Under Review).
- Conducted extensive experiments with mBERT, XLM-RoBERTa, and fine-tuned LLMs for low-resource language processing.
NLP Researcher (DravidianLangTech 2025)
CUET NLP Lab
Abusive Language Detection in Tamil
- Developed transformer-based approaches for detecting abusive Tamil text targeting women on social media as part of the DravidianLangTech 2025 shared task at NAACL.
- Implemented and fine-tuned multilingual transformer models (mBERT, XLM-R) for code-mixed and low-resource language classification.
- Paper accepted at NAACL 2025 DravidianLangTech Workshop.
NLP Researcher (LT-EDI 2025)
CUET NLP Lab
Hate Speech and Misogynistic Meme Detection
- Built multimodal frameworks for detecting misogynistic memes in Chinese online content and hate speech detection in code-mixed social media data.
- Developed transformer-based models for caste and migration hate speech detection across multiple languages.
- Published three papers at the LT-EDI 2025 workshop covering hate speech, misogynistic memes, and racial hoax detection.
Research
Publications
Workshop papers centered on transformer-based and multimodal NLP systems for social media analysis.
Experience
Production AI work.
Enterprise-grade agent systems, RAG pipelines, cloud automation, and full-stack delivery.
Software Engineer I
Brain Station 23
- Designed and enhanced enterprise AI Agents and RAG-based systems using OpenAI, AWS Bedrock, FastAPI, and Python.
- Built advanced RAG pipelines with semantic search, hybrid retrieval, document ingestion, and knowledge-base integration, improving search relevance and information accessibility.
- Integrated AWS (S3, Textract, Bedrock, Translate, Polly, Transcribe, CloudWatch) to automate document processing, translation, monitoring, and AI-driven workflows.
- Boosted scalability via Redis caching, connection pooling, and hybrid cache revalidation, significantly reducing latency and infrastructure overhead.
- Delivered enterprise web apps with React, Next.js, TypeScript, WebSockets and implemented AI observability using Langfuse tracing and analytics.
Internships & Training
Geeky Solutions Learnathon 3.0
Organized by Brain Station 23
- Implemented ASP.NET Core Web API with best practices, including Clean Architecture, Monolithic Pattern, CRUD, Authentication, Authorization, Global Exception Handling, Serilog, Documentation, Pagination, and more.
- 2-week industrial attachment focused on implementing ASP.NET Core Web API.
- Gained experience in Clean Architecture, Monolithic Pattern, CRUD operations, Authentication, Authorization, Global Exception Handling, Serilog, Documentation, Pagination, and more.
Capabilities
Skills & Expertise
Languages & Frameworks
AI & ML
Cloud & Tools
Milestones
Achievements
1st Place on the .NET Leaderboard, Learnathon 3.0 organized by Brain Station 23, achieving a 98/100 SonarCloud score (2025).
Dean's Award, Department of CSE, CUET – Recognized for outstanding academic performance among top students.
12th Place, Mujib Borsho Programming Contest 2022 – CUET Computer Club.
17th Place, CUET Intra-University Programming Contest (Divisional), 2022.
160th Place, ICPC Dhaka Regional Preliminary Online Contest, 2021–2022.
Problem Solving
Solved across different competitive programming platforms
Selected Work
Projects:
Professional AI systems, enterprise applications, and full-stack engineering work.
Project 01
Nettalliansen
Drove major architectural and production-level improvements across the Netti+ platform, including system refactoring, critical bug resolution, feature integration, hybrid caching, chatbot UX and accessibility enhancements, and Qdrant optimization. Customized and extended Langfuse for organization-level analytics, implementing privacy-preserving metrics and multi-tenant observability while maintaining end-to-end full-stack ownership. Proposed and implemented custom Qdrant indexing with file-upload-based indexing, improving document retrieval and search capabilities. Key Strength: Production AI Engineering, Architecture, Observability & System Optimization, Full-Stack Ownership.
Project 02
Thaugland
Designed and implemented the Skill Management Agent and its end-to-end lifecycle, including multi-agent orchestration, dynamic skill loading, AI-driven skill improvement, proposal and version management, and human-in-the-loop approval. Took full-stack ownership by implementing core frontend features, resolving critical bugs, and preparing the application for both development and production environments. On the backend, developed key features and contributed to multi-agent workflows and AI orchestration. Also applied AI-DLC practices to improve development efficiency, debugging, and solution design. Key Strengths: Full-Stack Engineering, Multi-Agent Architecture & AI-Assisted Development, AI-DLC.
Project 03
IntelliDocuMind
Engineered an end-to-end 9-service AWS AI pipeline, integrating S3, Textract, Bedrock, Knowledge Bases, Nova, Translate, Polly, Transcribe, CloudWatch, and OpenSearch. Contributed to RAG-based document intelligence, automated document processing, multilingual AI capabilities, and intelligent information extraction, gaining hands-on experience across the complete cloud AI lifecycle. Key Strengths: Cloud AI Architecture, AWS Integration & RAG Engineering, Full-stack Ownership.
Project 04
Unified Booking Management Platform Bot
Smart booking assistant for hotels, restaurants, services, and events. Features hotel availability and booking, booking cancellations with admin approval, Stripe payment integration with automated verification, confirmation emails, and instant notifications. Admin features include secure authentication, view/update/delete bookings, and approve cancellation requests. Implementation highlights include multi-API integration, automated payment and email flows, adaptive cards for confirmations, and secure access layers.
Project 05
HR Virtual Assistant
Virtual assistant for HR-related queries and company information. Employee features include company info, benefits and insurance details, leave policies and requests, PM clock entry, device requisitions, provident fund and loans, and payslip access. HR features include viewing and approving leave requests, managing employee info, and smart approval workflows with instant notifications. Backend includes real-time calculations, automated workflows, and instant updates to employees.
Project 06
Task Tracker
Full-stack enterprise-grade employee and task management system developed as part of Geeky Solutions' Learnathon 2024 (Team Dev-4545). Built with .NET 8 and React 18.2 using Clean Architecture and CQRS. Features RBAC, task assignment and progress tracking, leave application workflow, salary management, real-time Redis caching, reporting dashboard, and Swagger-based API documentation. Complete CI/CD pipeline via GitHub Actions with SonarCloud static analysis.
Contact
Get in Touch
I'm always open to new opportunities, collaborations, and interesting conversations.