Help & Supportcareers@gravityer.com
AI Engineer with 1+ years in Deep Learning & LLM Integration
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Machine Learning engineer with hands-on experience in deep learning, LLM integration, and production API development using PyTorch and FastAPI. Research background in federated learning, adversarial ML, and computer vision. Experienced building AI-powered backend services and deploying models for real-world applications. Strong mathematical foundation with national-level Physics Olympiad recognition.
Bangladesh University of Engineering and Technology
M.Sc. · EEE (Communication and Signal Processing)
August 1, 2025 – Present
Bangladesh University of Engineering and Technology
B.Sc. · EEE (Communication and Signal Processing)
August 1, 2019 – June 30, 2025
Bangladesh University of Engineering and Technology (BUET)
Graduate Teaching Assistant (EEE)
July 1, 2025 – December 31, 2025
Dhaka, Dhaka Division, Bangladesh
Movie Recommendation System
June 28, 2026 – Present
Built a collaborative filtering recommender using Matrix Factorization with SGD and L2 regularization on the MovieLens dataset to predict missing ratings
View ProjectUVC Disinfection Bot
June 28, 2026 – Present
Designed and prototyped a cost-effective autonomous UV-C disinfecting car using Arduino UNO, PID control, and sensors, with mapping and normal modes for navigation.
View ProjectBangla Document Q&A System (Multilingual RAG with OCR)
June 28, 2026 – Present
Built an end-to-end Retrieval-Augmented Generation (RAG) pipeline for Bangla and English PDFs, using multilingual sentence-transformer embeddings (paraphrase-multilingual-MiniLM-L12-v2) stored in a ChromaDB vector database. Added OCR support (Tesseract, Bengali language pack + Poppler) to extract text from scanned/image PDFs, with automatic detection of text-based vs scanned documents. Implemented hybrid retrieval combining semantic vector search with BM25 re-ranking, and generated grounded, source-cited answers via the Groq LLM API (Llama 3.1) in the question's language. Developed a FastAPI backend with a Streamlit frontend and containerized the full system with Docker (single image, offline-baked model) for reproducible deployment.
View ProjectAI-Powered University Admission Assistant
June 28, 2026 – Present
Developed an AI-based admission assistant to help students determine suitable university programs based on their academic profile. Built a REST API using FastAPI for handling student queries and admission recommendations. Integrated LLM-based reasoning using the Groq API (Llama 3) to generate intelligent admission guidance. Designed backend architecture with Python, FastAPI, and SQLite relational database integration.
View ProjectFrequency Division Multiplexing Using Analog Electronic Components in PSpice
June 28, 2026 – Present
Simulated a frequency division multiplexing system in PSpice to study analog signal processing and communication techniques.
View ProjectElectronic Voting System using FPGA Board
June 28, 2026 – Present
Implemented a secure electronic voting system on an FPGA board, focusing on hardware design and Verilog programming.
View ProjectSimulator of Damped Spring Mass System
June 28, 2026 – Present
Created a numerical simulator to model and analyze the dynamics of a damped spring-mass system using MATLAB.
View ProjectCultural Fit Analysis
The candidate's project diversity, ranging from advanced AI systems to embedded systems and analog electronics, indicates a broad intellectual curiosity and adaptability. Their academic background in Electrical and Electronic Engineering with a focus on Communication and Signal Processing provides a unique interdisciplinary perspective valuable in complex AI roles. The research experience in federated learning and digital twins suggests a collaborative and research-oriented mindset. The candidate's personal projects demonstrate initiative and a drive to apply theoretical knowledge to practical problems, which is a strong cultural fit for a dynamic AI engineering team.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills, evidenced by their academic achievements and diverse project portfolio. Their experience as a Graduate Teaching Assistant suggests an ability to communicate technical concepts and mentor others. The project descriptions are clear and detailed, indicating good written communication. The candidate's interest in cutting-edge AI topics like federated learning and LLMs aligns well with an innovative operational environment.