Shamsuddin Ahmed

I’m a software engineer from Dhaka, Bangladesh, with five-plus years spent on the same stubborn question: how do you take something that works in a notebook and make it survive real users?

Most of my work sits in three places — backend systems, production machine learning, and the harness layer that lets a language model safely operate software that already exists. Lately a fourth has crept in: machine learning for biology, where the datasets are small, the labels are noisy, and being honest about your numbers matters more than beating a leaderboard.

Right now

Senior Software Engineer — Mevrik

Dhaka · since August 2026

Mevrik is an AI-powered customer-experience platform used by telecom operators and hundreds of smaller businesses. I work on the AI side of the product: agents, retrieval, and the serving infrastructure underneath them.

Founder — AlgolyzerLab

A small studio I run for applied-AI work that doesn’t fit neatly into a product roadmap: sports-science tooling for elite cricket, clinical case-management software for doctors, agriculture AI, and research engineering. Small team, real deployments, unglamorous problems.

Before that

Senior Software Engineer → Software Engineer, Evoclick — Moscow (remote) · 2023 – 2026

Deployed quantised Qwen 3.5 27B (INT4 / FP8) on vLLM for a workload serving 1,000+ concurrent users, and built the OpenAI-compatible gateway that routed traffic to it. Shipped a multi-tenant AI orchestration platform with a DAG workflow engine, layout-aware (“page-index”) RAG for document understanding, LoRA/QLoRA fine-tunes for domain tasks, and a YOLO edge-vision pipeline running on NPU hardware.

Software Engineer, OMNAIBLE — Amsterdam (remote) · 2022 – 2023

ML-powered recommendation microservices in FastAPI, plus the caching and query work that made them fast enough to keep.

Software / DevOps Engineer, CodeSmith Tech — Dhaka · 2021 – 2022

Docker-based CI/CD, hardened REST and GraphQL APIs, and IoT data-ingestion backends.

What I’m thinking about

Agent harnesses. A model is only as useful as the surface you give it. I’m convinced most teams hand an LLM the wrong thing — raw tables and auto-generated OpenAPI specs instead of the intent-named operations their own code already has. That idea drives Reins, a small harness you bolt onto an app so a model can drive it safely, and Veldra, where an agent is stored as data — a versioned spec row — rather than code.

Machine learning for biology. I work on anti-inflammatory peptide prediction (AIPpred-Stack, with collaborators at the University of Saskatchewan — IEEE CCECE 2026) and on structure-aware virtual screening of natural products against understudied viral proteases (DeepNatProtease). Both taught me the same lesson: the dataset you choose decides your result long before the model does.

Systems that stay explainable. Fennec (SSIM-guided image compression in Go), VoidMon (a terminal system monitor), Clawkido (actor-model agent swarms) — small, readable, zero-magic tools I actually use.

Tools I reach for

Writing

I write to understand things, not to look clever. The posts here explain hard ideas the way I wish someone had explained them to me — with analogies, runnable code, and honest numbers. Start with the blog archive, or read the CV if you’d rather see the short version.

Say hello: info@shamspias.com · GitHub · LinkedIn