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Artificial Intelligence & Machine Learning Engineer

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About

Who am I?

ML engineer focusing on fraud detection, anomaly detection, and MLOps. I love reliable data pipelines and monitoring dashboards.

Toolbox

Python, SQL, Docker, MLflow, Airflow, Grafana.

Python Pandas NumPy Scikit-learn TensorFlow PyTorch SQL/MySQL Airflow Docker MLflow Git Linux

Works

Case Study — Fraud
Project 1
Short one-liner about the solution & impact.
Case Study — Forecast
Project 2
Short one-liner about the solution & impact.
Case Study — NLP
Project 3
Short one-liner about the solution & impact.
Case Study — Vision
Project 4
Short one-liner about the solution & impact.
Case Study — MLOps
Project 5
Short one-liner about the solution & impact.
Case Study — GenAI
Project 6
Short one-liner about the solution & impact.

Services

4 Developers

AI for IT Operations

Monitoring, analytics, auto-remediation.

5 Developers

Generative AI Development

Text/image apps, workflows, prototyping.

2 Developers

Data Engineering

Pipelines, feature stores, quality checks.

3 Developers

Model Monitoring

Drift, alerting, evaluations, dashboards.

Resume

ML Engineer — Zoibit
2023 — Present

Built fraud models; designed MLOps pipelines.

Data Analyst — ABC
2021 — 2023

Dashboards, SQL, forecasting.

Intern — DS Lab
2020

Text classification POC.

Skills

Python Pandas NumPy Scikit-learn TensorFlow PyTorch SQL/MySQL Airflow Docker MLflow Git Linux

Blog

Productionizing an XGBoost model
From notebook to CI/CD in 7 steps
Detecting drift in tabular data
KS tests, PSI, and practical thresholds
Serving GenAI safely
Rate limits, red-teaming, guardrails

Contact