$ automation.status() → active

I turn manual work into pipelines that run themselves.

Python automation and machine learning for teams tired of doing the same task twice. I design, build, and ship the systems that quietly do the work — so you don't have to.

0 pipelines shipped
0 hours automated / month
0 avg. model accuracy
0 client retention

Replace these with your real numbers before publishing.

# what I automate

One skill set, four ways it pays off

Data pipelines & ETL

I connect your scattered sources — APIs, spreadsheets, databases — into one clean, scheduled flow that lands where you need it, on time, every time.

Machine learning models

From forecasting to classification, I build models that hold up outside the notebook — trained, tested, and wired into something your team actually uses.

Workflow automation

The repetitive clicks, copy-pastes, and status-checking your team does by hand — I script it away and hand you back the hours.

API & system integration

Two systems that should talk to each other but don't — I build the bridge, so data moves without anyone re-typing it.

# how it gets built

Five steps, no guesswork

01

Discover

We map the current process end to end — what's manual, what breaks, what it's costing you in hours.

02

Design

I sketch the pipeline: inputs, transformations, decision points, and where a model earns its place.

03

Build

Python, tested as I go. You see working pieces early, not a black box at the end.

04

Deploy

Scheduled, containerized, and monitored — running on its own, not on someone remembering to click "run."

05

Monitor

Alerts on failure, logs you can read, and a handover doc so the system outlives the project.

# selected work

Proof, not promises

Logistics · ETL

Same-day order sync across 3 warehouses

Replaced a nightly manual spreadsheet merge with a scheduled Python pipeline reconciling stock across systems.

92%faster reconciliation
PythonAirflowPostgreSQL

Retail · ML

Demand forecasting for seasonal stock

Built a forecasting model that cut overstock without increasing stockouts, retrained automatically each week.

31%less dead stock
scikit-learnPandasDocker

SaaS · Workflow

Support ticket triage on autopilot

A classifier routes and tags incoming tickets before a human ever opens them, with a fallback queue for edge cases.

18hrssaved per week
PythonFastAPINLP
AN

# who's building this

Amir Nadeem

I'm a Python developer who specializes in automation and machine learning — the layer of work between "we have the data" and "the system handles it." I've spent my career turning manual, error-prone processes into pipelines that run quietly in the background, and models that hold up once they leave the notebook.

I care less about the fanciest algorithm and more about whether the thing still works at 3am when nobody's watching it.

Languages
PythonSQLBash
ML & Data
Pandasscikit-learnPyTorchNumPy
Automation & Infra
AirflowDockerFastAPIAWS

# let's talk

Got a process that's still manual?

Tell me what it is. I'll tell you honestly whether automating it is worth it.

hello@amirnadeem.dev
in gh x