Software Engineer with 4+ years of experience specializing in architecting high-performance microservices using Golang & Python, and building intelligent LLM applications.
Developing scalable cloud-native architectures, AI-integrated workflows with LangChain, and empirical machine learning models published with Springer Nature.
Engineering scalable AI backends, distributed systems, and low-latency inference runtimes in high-throughput environments.
Engaged in advanced research on machine learning applications, neural representation modeling, and scalable system backends.
Responsible for end-to-end design, development, and deployment of the Agricultural Lab Management and Reporting System (ALMRS) under the Ministry of Agriculture, reporting directly to the Scientific Officer.
Delivered polyglot full-stack systems and microservices for international clients, focusing on scalable REST APIs and modern web applications.
Worked as a Research Assistant in the Faculty of Science & Technology during final year, contributing to department research initiatives.
Architected the complete backend in Go (Golang) with PostgreSQL. Features automated laboratory workflows, client sample lifecycle management, reference standards evaluation, Bengali PDF generation for official soil test reports, and secure government audit logging.
Modular microservices backend utilizing LangChain and Python for intelligent data extraction from unstructured receipts, SMS, and emails, paired with Java Spring Boot services.
Unsupervised NLP framework leveraging BERTopic and BGE-Base-en-1.5 embeddings to uncover latent thematic structures across large-scale news article corpora.
Sound-activated autonomous robotic vehicle with ultrasonic obstacle avoidance, environmental temperature sensing, and real-time Bluetooth phone telemetry.
Machine learning classification pipeline analyzing multi-factorial clinical indicators using Naïve Bayes and comparative classifiers to predict stroke onset risk.
Rafin Abrar Rono, Md. Sayem Kabir, Sadman Samir Rafith, Md. Bakibillah Rahat, Tasnim Sultana Sintheia, Kazi Tanvir
Md. Bakibillah Rahat, Richard Victor Biswas, Nafisa Maliyat
Active trajectories, upcoming paper submissions, and foundational systems under continuous exploration for upcoming PhD and research lab milestones.
Developing a novel speculative drafting kernel combining sparse neuromorphic temporal spikes with high-density FP8 matrix units for real-time generative sequence models.
Formalizing cryptographic verification proofs for multi-agent reasoning graphs to eliminate context tampering, malicious tool prompt injection, and untrusted state injection.
Designing an analytical sensitivity metric that dynamically selects layer-wise quantization depth at runtime based on input perplexity and memory bandwidth pressure.
A bare-metal, dependency-free C++ runtime for compiling and executing Spiking Neural Networks on ultra-low-power microcontrollers and edge silicon.
Investigating consensus-driven weight aggregation protocols that guarantee Byzantine fault tolerance without requiring centralized parameter server trust.
Conducting advanced research in machine learning and distributed systems for intelligent applications.
Enhancing containerized LangChain extraction microservices in Spring Boot and Python.
Publishing technical analyses on caching, database replication, and load-balanced microservices.
I am a Software Engineer dedicated to building scalable, high-performance backend systems and production machine learning applications.
With over 4 years of hands-on experience, I specialize in architecting distributed microservices using Go (Golang) and Python. My work spans designing mission-critical enterprise platforms—such as the Agricultural Lab Management and Reporting System (ALMRS) under the Ministry of Agriculture—to developing AI-integrated solutions with LangChain, Docker, and PostgreSQL.
On the research front, my work in transformer-based topic modeling has been published by Springer Nature ('Proceedings of the 3rd International Conference on Big Data, IoT and Machine Learning'). I was also honored to be selected as a top finalist in the Harvard-founded Aspire Leaders Program from over 54,000 global applicants.
Whether collaborating on production engineering roles, enterprise architecture consulting, or academic research, I bring polyglot programming rigor, systems thinking, and end-to-end execution to complex technical challenges.
From undergraduate honors and selective Harvard leadership training to active graduate research in Computer Science.
Enrolled in the Master of Science in Computer Science (MScCS) program at AIUB (Admitted: 03-Jun-2026). Currently undertaking advanced graduate coursework across machine learning, neural networks, blockchain cryptography, and modern computing advancements.
Selected as a top finalist in a prestigious global academic and leadership development program founded by Harvard University faculty. Completed HarvardX coursework, leadership simulations, and capstone social impact project with mentorship from Harvard faculty.
I am open to discussions regarding AI Systems research collaborations, funded PhD opportunities (Fall 2026 / 2027), and specialized machine learning engineering initiatives.