Understand before choosing tools
The workflow and its failure cases come first. Technology is selected after the real constraint is visible.
I work where browser behavior, APIs, data, AI, and servers intersect.
That overlap is usually where a clean demo meets messy reality. The source changes. The session expires. A model returns the wrong shape. A job stops halfway through. I build for those parts.
Test the risk early
If the project depends on a protected website, unreliable document, or awkward integration, prove that part before polishing the rest.
Make failure explicit
Logs, retries, validation, status, and escalation paths are part of the product, not follow-up tasks.
Leave an ownable system
Maintainable code, portable deployment, and clear operating notes matter because somebody has to run it next month.
Background
Five-plus years across automation and production systems.
Sujit works independently across Python automation, scraping, backend workflows, bots, data pipelines, deployment, and server operations. Technical choices are explained in plain language, with the difficult tradeoffs made visible early.
He holds a bachelor's degree in Computer Science from Dhaka International University and works remotely from Bangladesh in UTC+6.
Rust is a learning track—not a claim of expertise—driven by an interest in performance, memory safety, and reliable backend tooling.
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