About Me

A passionate Programmer & Software Developer with a love for creating elegant solutions to complex problems

Jitu

Md. Jidanul Hakim Jitu

Experience

Software Developer

ShiftLayer (USA / Remote)

Nov 2025 - Present

Technical Consultant

DevBucket (Hybrid / Part-time)

Nov 2025 - Present

Software Developer

Athena CRM | PLAC-D, London (Remote)

Apr 2022 - Sep 2025

View Details

Activities

AIUB Computer Club (ACC)

React & React-Native Journey Session Leader

January 2022 - May 2022

Currently working in Research and Development (R&D) section

Visit ACC

About Me

I am a goal-oriented programmer and problem-solver with energy for software development, who might want to join a team of similar engineers. I have a lot of involvement in making both legitimate and creative answers for complex Programming issues.

Problem Solver

Full Stack

Team Player

Fast Learner

Education

Bachelor's Degree

B.Sc. in CSE

CGPA: 3.92

Major: Software Engineering

American International University-Bangladesh

2020 - 2024

Higher Secondary

H.S.C.

Science

Chattogram Bandar College

2019

Secondary

S.S.C.

Science

Government Muslim High School

2017

Research & Thesis

Large-Scale Safety Evaluation of Public LoRA Modules

Independent Research

Examining how LoRA-based fine-tuning impacts safety and alignment in LLMs. Evaluating 100+ public modules for toxicity, jailbreak susceptibility, and demographic bias amplification. (In Progress)

Research Achievements
  • Evaluating 100+ public LoRA modules
  • Assessing toxicity and jailbreak susceptibility
  • Measuring demographic bias amplification
  • Analyzing safety and alignment in LLMs
Technologies Used
PythonLLMsLoRAMachine LearningSafety & Alignment

Advancing NLP Tasks Through Fine-tuning of Foundation Language Models

Undergraduate ThesisSupervisor: Sharfuddin Mahmood

Authors: Md. Jidanul Hakim Jitu, Nahim Hossain Shohan, MST. Rokeya Khatun, Mahamoda Rupa

A comprehensive study on improving Natural Language Processing tasks through enhanced fine-tuning techniques of GPT models, focusing on efficiency, performance, and practical applications.

Research Achievements
  • Developed adaptive few-shot learning techniques
  • Achieved 86.7% accuracy on IMDB sentiment analysis
  • Improved efficiency in fine-tuning process
  • 24-week intensive research project
Technologies Used
PythonNLPMachine LearningGPT ModelsFine-tuning

Expertise

Technologies I use daily and have mastered

NextJSReactReact-NativeJavaScriptTailwindStyled-ComponentRTK-QueryPythonShadCNMaterial UIHTMLCSSTypeScript

Comfortable

Technologies I'm proficient with and use regularly

DSAOOPReact QueryReduxAnt DesignNodeJSExpress JSNest JSMongoDBGITGitHubC#C++Postgres/MySQLMongooseJAVASpring (JAVA)

Familiar

Technologies I've worked with and continue to explore

ZustandRecharts (D3 JS)NetlifyFirebaseHerokuSASSGSAP