Sium Ibn Akhter
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Operator Engineer Automator Auditor

Sium Akhter

Sium Ibn Akhter
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The Method

Clean systems are a competitive advantage.

I am a computer science graduate who ended up in revenue operations because that is where the interesting failures live. Across more than a hundred engagements in the US, UK, Canada and Australia the pattern repeats: the dashboard is confident, the data underneath it is not, and nobody has looked in months. I go and look. Then I rebuild the process so the same failure cannot happen twice.

  • 01

    Look where review already passed

    Standing procedures catch what they were written to catch. The failures that matter clear every check and still corrupt the number. Two of those, in one live system of 12,000 records, had gone unnoticed for months.

  • 02

    Remove the cause, not the symptom

    Repairing 1,789 broken records is a morning of work. Finding the duplicate field that zeroed them is the actual job. I ship the second one.

  • 03

    Automate to a runbook, never a black box

    Every build ships with an SOP. If the system only works while I am in the room it is not a system, it is a dependency.

  • 04

    Fewer moving parts beats more coverage

    Six competing processes became one with six branches. Eighty automation rules became twenty-seven. Same coverage, 83% fewer evaluations, no execution-order risk.

Experience

One company, four roles, 50+ clients, 300+ projects.

Get Levrg / Dhaka
    Selected Engagements

    The systems behind the number.

    04 / Case Studies

    Client names withheld on individual case studies. Full roster below.

    Systems & Tools

    Things I built so nobody repeats the work.

    08 / Builds
    By The Numbers

    Two years, measured.

    The Stack

    What I work in.

      Client Roster

      Who I have worked with.

      Credentials

      Paper and proof.

      Certifications
        Education

          Languages  /  Bengali (native)  ·  English (professional working)

          Optimizing Citrus Leaf Disease Detection: An Efficient Custom CNN Leveraging Training Parameters

          2024 IEEE International Conference on Artificial Intelligence in Engineering and Technology. A custom CNN reaching 97.84% validation accuracy and a 96% F1 score with substantially fewer training parameters than ResNet-50 or VGG16, built for resource-constrained environments.

          View publication ↗