Md. Seamul Islam (Seam)
Software Engineer — Backend (Python / Django, DRF)
Backend-focused engineer with 5+ years shipping production APIs, async pipelines, and data-heavy platforms — from AutoML systems to CRM automation and e-commerce. Comfortable owning a service end-to-end: schema design, integrations, deployment, and performance.
Experience
Backend development and platform engineering, building and maintaining production services.
Designed and shipped backend services and automation tooling; worked across APIs, integrations, and CRM-style platforms.
Backend engineering with Django/DRF; async tasks with Celery; relational data modeling and APIs; deployment and performance tuning.
Feature development and maintenance across backend services; API integrations; testing and documentation.
Django-based internal tools and services; database queries; code reviews and CI basics.
Highlighted Projects
Meta integrator & marketing automation CRM — unifies Meta channels for lead capture, conversational automation, and end-to-end CRM workflows.
End-to-end platform for managing tour packages, bookings, itineraries, and customer workflows.
E-commerce platform covering catalog, cart, checkout, and order management.
AutoML platform for non-experts. Role: backend services, job orchestration, model lifecycle APIs.
AutoML programme for rapid AI insights. Responsible for API endpoints and dataset handling.
ML for Oil & Gas; built ingestion & processing pipeline and delivered analytics views.
Data engineering + forecasting APIs and dashboards.
Ingests CSV/Excel and computes account summaries with audit trails.
Technical Skills
Comfortable designing RESTful services, database schemas, async workers, and deploying to cloud servers.
Education
B.Sc. in Information & Communication Technology
Mawlana Bhashani Science & Technology University (MBSTU), 2017.
Publications
"Proposal of a New Method in Image Encryption and Decryption Technique."
IEEE SPICSCON 2019.
Introduces the Seam's Random Number (SRN) generator — a novel pseudo-random number generation method proposed by the author — combined with bitwise XOR operations to build a secure image encryption and decryption scheme. The SRN-based approach achieves high mean square error (MSE), a uniform histogram distribution, low pixel correlation, and high entropy in encrypted images, enabling robust, lossless decryption and image reconstruction.
Competitive Programming
250+ problems solved across UVA/URI/Codeforces; occasional contests & blogging.