I am Rakibul Islam — an AI Software Engineer & Researcher specializing in Deep Learning, Multi-Agent Systems, Graph Neural Networks (GNNs), and LLM-powered enterprise architectures.
Bridging cutting-edge machine learning research with industrial-grade software engineering. My work focuses on scalable reasoning, computer vision, and context-grounded agentic workflows.
Authored 7 international publications indexed in IEEE, ACM, and ICTIS, alongside 2 manuscripts currently in preparation for high-impact venues.
Combined custom YOLOv8 backbone architectures with deep ensemble CNNs to achieve real-time fabric surface inspection with sub-millimeter precision in industrial factory pipelines.
Engineered multi-modal fusion networks combining amino acid sequence encodings with structural embeddings to predict Gene Ontology functional terms.
Formulated statistical covariance and non-linear correlation filtering algorithms to maximize dimensional reduction efficiency on high-variance tabular datasets.
Developed an attention-guided UNet++ variant with adaptive pixel thresholding to isolate pulmonary infiltrates in low-contrast chest radiography.
Evaluated transfer-learning feature extractors over multi-center clinical datasets for automated pre-symptomatic tuberculosis triage.
Proposing multi-head self-attention pooling over GCN representations to solve node-permutation-invariant graph edit distance and structural similarity.
Fusing local convolutional priors with global Vision Transformer token patches for high-speed anomaly detection under severe occlusion.
Industrial-grade AI applications integrating local LLMs, autonomous multi-agent swarms, and high-throughput vision pipelines.
A fully private, local knowledge ingestion engine that indexes unstructured enterprise documents and SQL databases to provide context-grounded citations with conversational memory.
An end-to-end automation platform orchestrating multiple CrewAI autonomous agents for resume tailoring, semantic cover letter synthesis, and verified application dispatch.
Edge-deployed computer vision system deploying custom YOLOv8 bounding algorithms backed by deep ensemble convolutional classifiers for high-speed manufacturing quality control.
Engineered core operational modules for enterprise Human Resource Management, implementing Repository Pattern, Unit of Work, automated reporting, and intelligent agent integrations.
A multi-disciplinary stack engineered across modern agentic paradigms, deep neural modeling, and enterprise backend platforms.
Proven industry contributions with rapid performance-based promotions, startup technical leadership, and formal computer science education.
Promoted from Python Intern to full Software Programmer based on execution speed and technical depth. Architected key automation pipelines for the Alfred platform and HRM software using ASP.NET Core 8 MVC, embedding autonomous CrewAI agents, RAG workflows, and local LLMs.
Built automated workflow microservices using FastAPI, Selenium, and Docker. Implemented database schema enhancements and early RAG proofs-of-concept.
Spearheaded technical vision, engineering roadmap, product delivery, and cross-functional engineering teams for early-stage enterprise software and client solutions.
Comprehensive academic background in Algorithms, Complexity Theory, Deep Learning, Statistical Machine Learning, and Distributed Computing. Active contributor to the UIU App Forum and Entrepreneurship Forum.
Whether you are interested in collaborating on GNN and Multi-Agent research, deploying enterprise RAG pipelines, or discussing AI engineering roles, my inbox is open.