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AI Research Engineer with 1+ years in LLM Workflows & Machine Learning
I build AI systems that automate workflows, analyze complex data, and solve real-world operational problems. Built deployable applications involving LLM workflows, agentic AI systems, forecasting pipelines, anomaly detection, computer vision, analytics dashboards, optimization systems, and backend AI services using Python, FastAPI, PyTorch, TensorFlow, SQL, Docker, and Streamlit. Experienced working across the full ML lifecycle including data engineering, feature engineering, experimentation, model evaluation, inference pipelines, workflow automation, forecasting, analytics engineering, and production-style deployment on noisy real-world datasets. Strong focus on building scalable and usable AI systems rather than research-only prototypes, with hands-on experience developing intelligent automation workflows, deployable ML systems, and AI-powered applications used for real-world monitoring, prediction, and decision support.
American International University-Bangladesh
BSc. · Electrical and Electronic Engineering (EEE)
N/A – June 30, 2024
Dhaka Commerce College
Higher Secondary Certificate · Science
N/A – Present
ReGed
Data Intelligence Researcher
February 1, 2025 – Present
India
LegalDraft-AI
June 1, 2026 – Present
Built an AI-powered legal document drafting and automation system using modern LLM workflows and backend AI services. Developed intelligent document generation pipelines supporting automated drafting, structured legal workflows, and AI-assisted content generation. Designed scalable AI workflow orchestration pipelines integrating prompt engineering, retrieval-style workflows, backend APIs, and deployable AI infrastructure. Focused on production-oriented AI application architecture, workflow reliability, and real-world automation usability.
View ProjectAI-Based Livestock Health Monitoring System
June 1, 2026 – Present
Built deployable AI monitoring platform using thermal imaging and deep learning for early livestock disease detection and anomaly analysis. Designed preprocessing and augmentation pipelines for noisy real-world thermal datasets collected under changing environmental conditions. Built real-time inference dashboards and monitoring workflows using Streamlit and automated anomaly visualization systems. Generated synthetic seasonal datasets to improve model robustness, forecasting reliability, and generalization across unseen conditions. Focused on production-oriented AI workflows prioritizing deployment reliability and real-world monitoring performance.
View ProjectPower Transformer Fault Diagnosis using DGA
June 1, 2026 – Present
Built intelligent fault diagnosis pipelines for electrical infrastructure using Dissolved Gas Analysis datasets and machine learning classification systems. Performed feature extraction, classification modeling, and evaluation workflows achieving 95%+ prediction accuracy. Designed reproducible experimentation workflows involving feature engineering, model comparison, validation strategies, and reliability-focused optimization. Published engineering research based on the developed ML framework and predictive analytics system.
View ProjectFabric Quality Detection System
June 1, 2026 – Present
Built computer vision pipeline for automated fabric defect detection and industrial quality inspection workflows. Developed image preprocessing, feature extraction, and deep learning classification pipelines for real-world manufacturing datasets. Designed scalable inspection workflows supporting anomaly detection, defect classification, and automated visual analysis. Focused on deployable industrial AI workflows prioritizing inference reliability and production usability.
View ProjectTime-Series Forecasting & Intelligent Prediction Systems
June 1, 2026 – Present
Built forecasting systems for environmental and engineering datasets using LSTM architectures and ensemble learning workflows. Engineered temporal feature pipelines for trend extraction, seasonality modeling, anomaly detection, and predictive optimization. Improved forecasting performance through structured experimentation, hyperparameter optimization, and evaluation-driven iteration. Designed reusable preprocessing and sequential inference workflows supporting scalable time-series prediction systems.
View ProjectData Science Certificate
Ostad Ltd
June 1, 2026 – Present
Python Skill Certificate
Hacker Rank
June 1, 2026 – Present
Professional Certificate in Business Analytics
BCET
June 1, 2026 – Present
Python Development & Data Science
MTF Institute
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from legal document automation to livestock health monitoring and industrial quality detection, indicates a broad interest and adaptability in applying AI solutions across various domains. Their emphasis on 'production-oriented AI workflows' and 'real-world automation usability' suggests a pragmatic approach that would fit well within a team focused on delivering tangible impact. The experience with collaborative production-oriented AI systems also points to a team-player mindset.
Soft Skills & Operational Fit
The candidate demonstrates a strong 'Production AI Mindset' and 'Experimental Engineering' approach, which are crucial for operational fit in an AI Research Engineer role. Their focus on deployable, scalable, and reliable AI systems, coupled with structured experimentation, aligns well with industry best practices. The 'AI Workflow Thinking' and 'Real-World Data Experience' further highlight their practical and problem-solving orientation.