CV

Education

Master of Engineering, Information Technology (4.75 / 5.00)
Metropolia University of Applied Sciences, (2024 – May 2025)
Helsinki, Finland
Thesis: Using Open Source LLM Model for Medical Transcription
Bachelor of Engineering, Computer Science and Engineering (3.75 / 4.00)
Metropolitan University, (2016 – 2019)
Sylhet, Bangladesh
Thesis: Heart Disease Prognosis Using Machine Learning Classification Techniques

Work experience

AI Simulation Engineer (Jan 2025 – March 2026)
Huawei
Helsinki, Finland
- Technical project planning, proposal, and execution for consumer electronics devices, next generation battery technology.
- Develop AI and simulation technologies by integrating physics-based models with data-driven, transfer-learning and physics-informed machine learning approaches to improve characterization of consumer electronics devices and hybrid simulation approaches
- Advance lithium-ion battery research for consumer electronics by characterizing new cell technologies, modeling degradation mechanisms, and supporting improvements in online battery management systems.
- Built AI‑based characterization tools to analyze device behavior, performance patterns, and system reliability.
- Improved online Battery Management Systems (BMS) by developing predictive algorithms for SOH, aging, and performance.
- Designed simulation workflows combining physics‑based models with ML to enhance accuracy and reduce testing cycles.
- Collaborated with cross‑functional engineering teams to translate simulation insights into product improvements and design decisions.
AI Engineer (Oct 2024 – Dec 2024)
Digi Energia Oy
Helsinki, Finland
- Designed and deployed AI‑driven payment intelligence to improve transaction security, automate billing, and reduce fraud across EV charging networks.
- Built predictive ML models for pricing, demand forecasting, and customer behavior to support real‑time operational decisions.
- Integrated AI services across payment systems and charger IoT platforms, ensuring seamless data flow and optimized system performance.
- Developed end‑to‑end ML pipelines, from data processing to model deployment, supporting scalable FinTech and EV charging applications.
- Collaborated with engineering, product, and operations teams to embed AI capabilities into core business workflows.
- Monitored and improved model accuracy, ensuring high‑quality data, reliable predictions, and continuous system enhancement.
Senior Software Engineer (Mar 2023 – Sept 2024)
VALT
Milton Keynes, England, United Kingdom
- Developed complex, scalable REST APIs using NodeJS, Python, FastAPI, PostgreSQL, MongoDB, and Redis for high‑performance payment and EPOS systems.
- Integrated Stripe payment workflows, building secure custom payment handlers and automated billing pipelines.
- Designed and deployed ML solutions for core product intelligence, including pricing, recommendations, and operational automation.
- Built and optimized cloud‑native architectures on AWS and GCP using Lambda, EC2, Airflow, caching layers, and containerized services.
- Implemented CI/CD pipelines with GitLab and Git to ensure fast, reliable, and automated deployments.
- Developed scalable data pipelines to support analytics, model training, and real‑time decision systems.
- Collaborated with cross‑functional teams, including mobile, frontend, and product, to deliver end‑to‑end features.
- Improved system performance and reliability through caching strategies, API optimization, and distributed service design.
AI Engineer (Jan 2023 – Mar 2023)
Carbarn
Sydney, New South Wales, Australia
- Developed and maintained AI/ML models for pricing, demand forecasting, customer recommendations, and marketplace optimization.
- Built and improved search, ranking, and recommendation algorithms to enhance car‑buying and discovery experiences.
- Collected and analyzed competitor data using automated web‑scraping tools (BeautifulSoup, Scrapy, Selenium) to support strategic decisions.
- Processed and transformed large datasets with Airflow and Spark to generate insights for sales, marketing, and product teams.
- Collaborated with cross‑functional teams to integrate AI solutions into business workflows and customer‑facing features.
- Monitored model performance and data quality, ensuring continuous accuracy, reliability, and improvement of AI systems.
Lead Software Engineer (Dec 2019 – Dec 2022)
Ordervox
London, England, United Kingdom
- Led full‑stack development of scalable web, mobile, and desktop applications using Python, PHP, FastAPI, JavaScript, Java, and CodeIgniter for the OrderE restaurant automation platform.
- Architected and optimized cloud infrastructure across AWS Lambda, EC2, ECS, RDS, Redis, Docker, and Kubernetes to support high‑traffic operations for 300+ restaurant clients.
- Designed cost‑efficient backend systems and data pipelines, improving automation, performance, and reliability across ordering, delivery, and in‑house dining workflows.
- Managed an 8‑member core engineering team, driving agile execution, sprint planning, code quality, and on‑time delivery of complex product features.
- Enhanced system performance and scalability through caching strategies, PL/SQL optimization, and cloud‑native best practices to ensure a seamless customer experience.
Software Engineer Intern (Jan 2018 – Dec 2019)
Parking koi INC
Dhaka, Bangladesh
- Implemented and maintained Google Maps–based location and routing features for a car‑parking solution (Uber‑style), building RESTful APIs using PHP, CodeIgniter, JavaScript, and AJAX.
- Deployed and supported backend services on AWS, ensuring reliable performance, version control, and smooth feature delivery using Git.
- Integrated map interactions such as distance calculation, live position updates, and dynamic marker rendering for smooth user experience.
- Deployed backend services on AWS, ensuring stable performance, uptime, and secure environment configuration.
- Collaborated using Git for version control, contributing to feature development, bug fixes, and code reviews within the engineering team.

Skills

Languages: English (C1), Finnish (A2)
Programming/Framework: Python, Javascript, PHP, NodeJs, Shell, Postgre-SQL, C, C++, MySQL, Mongodb, Git, FastApi, Django, Codeigniter, RestAPI, CSS, Docker, Kubernetes
ML: MLflow, PyTorch, FastAPI, PyTorch, TensorFlow, Keras, and Scikit-Learn for computer vision, NLP, LLMs, MLOps, Airflow, and CI/CD (GitHub Actions, GitLab), (Most of ML model implementation)
Generative AI: Designed, fine-tuned, and deployed LLM and RAG-based applications, tokenization, vector search
Data Science: feature engineering, Snowflake, Spark, BeautifulSoup, Scrapy, Selenium, Azure Data Engineering tools, Data-bricks, PowerBI
Research: Overleaf, Lucidchart, Matlab, LaTeX, Rails, OCaml
Document Creation: Microsoft Office Suite, LaTex, Overleaf, Lucidchart

Publications

Evaluating Fine-Tuned LLM Model For Medical Transcription With Small Low-Resource Languages Validated Dataset

Mohammed Nowshad Ruhani Chowdhury (2026). "Evaluating Fine-Tuned LLM Model For Medical Transcription With Small Low-Resource Languages Validated Dataset." Arxiv Journal. 1(3).

Using Open Source LLM Model for Medical Transcription

Mohammed Nowshad Ruhani Chowdhury. (2025). "Using Open Source LLM Model for Medical Transcription." Metropolia University of Applied Sciences Journal. 1(3).

ANOVA-based automatic attribute selection and a predictive model for heart disease prognosis

Mohammed Nowshad Ruhani Chowdhury (2022). "ANOVA-based automatic attribute selection and a predictive model for heart disease prognosis." Journal IEEE Transactions on Big Data. 1(2).

Heart Disease Prognosis Using Machine Learning Classification Techniques

Mohammed Nowshad Ruhani Chowdhury, (2021). "Heart Disease Prognosis Using Machine Learning Classification Techniques." Conference 2021 6th International Conference for Convergence in Technology (I2CT).

Conferences and Lab Visit

Huawei Battery Lab’s China
(HQ Visit: Shanghai, Dongguan, China) (5–15 December 2025)
- Implement PINN model in cloud BMS.
- Implement Transfer Learning LSTM model in mobile BMS.
- Visit Huawei Battery Intelligence Lab, and check data collection process and analysis data.
- Check mobile BMS data, collected from end-user, and analysis data; 2M data.
AI in AEC 2025 Conference Paasitorni Congress Centre
Helsinki, Finland (2025)
2025 Huawei Future Deviece Technology Summit (Aurora Summit)
Oulo, Finland (2025)
2021 6th International Conference for Convergence in Technology (I2CT) IEEE Conference
Maharashtra, India (2021)

Research Team

Vision Intelligence and Data Lab,
Lab, (2021 – Present)
Ontario, Canada
Research group of Lakehead University’s for Machine Learning and AI on Domain: healthcare, battery, consumer electronic
Metropolia Medical Scribes Team,
Medical Scribes Team, (2024 – Spring 2025)
Helsinki, Finland
Research group of Metropolia University about Medical Scribe

Awards

Received Vice Chancellor Award (2018)
Recognition for to 3% of students in academics at Metropolitan University.
First Place in Mobile Application Development (2018)
After mobile application development training from Bangladesh ICT Division, August, 2018 To December 2018, achieve 1st place in the examination.
45th/190 (teams) ICPC Virtual Dhaka Regional Contest (2017)
Team award from ICPC.
5th/150 MU Intra University Programming Contest (2017)
Programming contest in the University.
1st Place Chess Olympiad, Bangladesh (2012)
Awarded from Bangladesh Chessboard.
5th/50 (teams) BUP Inter University Hackathon National Level Contest (2019)
Awarded from BUP University.

Service and leadership

Team lead at Ordervox; managed an 8‑member core engineering team,
Ordervox, (Dec 2019 – Dec 2022)
General Secretary,
Metropolitan University’s Cycling Association, (2018-2019)
Software Developer Secretary,
CSE Society, Metropolitan University (2018-2019)
Cadet Under Officer,
Bangladesh National Cadet Corps (MC Collage Platoon) (2014-2015)
Team Leader,
Bangladesh Red Crescent Society(School Platoon) (2011-2012)