Janet J.

Machine learning engineer

ML Team Lead
8 yrs experience
Technical Lead

Still actively listens to Micheal Jackson songs

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Available

Key Skills & Tools

Python
Model Deployment (MLOps)
Time Series Forecasting
MLflow
RESTful APIs

English profeciency

Fluent

Timezone available in

East Asia

Get to know me

I'm a Machine Learning Engineer with 8+ years of experience building and deploying scalable AI solutions across industries. I specialize in model development, deep learning, and MLOps, with a strong foundation in Python, cloud platforms, and cross-functional collaboration. I’m passionate about turning complex data into actionable insights and leading high-impact projects from prototype to production.

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Details about me

Skills that pay the bills

Work I'm proud of

Why Out of Office thin...

💼 Brain & heart

Skills that pay the bills

Tools

Python
R
SQL
RESTful APIs
AUC
Git & Version Control

Technical

PyTorch
MLOPs
Data Engineering
Big Data
Time Series Forecasting
MLflow
Bash/Shell scripting
Data Versioning
AUC

Domain Knowledge

Bash/Shell scripting
Data Versioning
AUC

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Work I'm proud of

Scalable ML infrastructure for Agro-Health Platform

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📌 Project Overview

Helped an Agro-Health startup scale its disease detection platform by building a robust ML infrastructure that processed satellite, soil, and weather data for real-time farmer insights.

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❗ The Problem

Siloed, unstructured data
Manual model training and deployment
No ability to scale across regions or retrain models easily

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🛠️ What I Did

Designed end-to-end ML pipeline using AWS SageMaker, Airflow, and MLflow
Built automated ETL pipelines to unify and clean multi-source agricultural data
Deployed real-time scoring API via FastAPI + Docker
Implemented CI/CD for weekly model retraining

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Impact

60% faster insights (10 mins vs 24 hrs)
🎯 89% F1-score for disease detection
🌍 Scaled to 8 regions
📲 +28% farmer engagement

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Personalization Engine for eCommerce Retailer

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📌 Project Overview

Helped an Agro-Health startup scale its disease detection platform by building a robust ML infrastructure that processed satellite, soil, and weather data for real-time farmer insights.

‍

❗ The Problem

Siloed, unstructured data
Manual model training and deployment
No ability to scale across regions or retrain models easily

‍

🛠️ What I Did

Designed end-to-end ML pipeline using AWS SageMaker, Airflow, and MLflow
Built automated ETL pipelines to unify and clean multi-source agricultural data
Deployed real-time scoring API via FastAPI + Docker
Implemented CI/CD for weekly model retraining

‍

Impact

60% faster insights (10 mins vs 24 hrs)
🎯 89% F1-score for disease detection
🌍 Scaled to 8 regions
📲 +28% farmer engagement

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Why Out of Office thinks I'm awesome

Working with Markus N. has been a masterclass in what it means to be a truly effective ML engineer. He combines an exceptional grasp of algorithms and model architecture with a deep understanding of infrastructure, scalability, and business impact. Whether it's designing robust pipelines, optimizing model performance, or translating technical results into clear stakeholder value, Markus consistently delivers with precision and insight.

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