Machine Learning Engineer — production ML systems, from raw data to deployed APIs
LinkedIn · Jaipur, India
I build ML systems end-to-end — not just notebooks. That means proper train/test discipline, model explainability, containerized deployment, and CI/CD: the parts of the job that don't show up on a leaderboard but do show up in production.
Currently working on marketplace and economic-data problems, with a focus on ML engineering roles at Japan-based companies — Mercari among them — and in Germany.
End-to-end pipeline predicting 15+ minute arrival delays across 5.8M US domestic flights (DOT/BTS 2015), from raw data to a served, containerized model.
- Data: 5.8M rows cleaned to 5.2M; class imbalance (4.4:1) handled via
scale_pos_weight, not resampling - Modeling: Logistic Regression → Random Forest → XGBoost, tuned with 3-fold GridSearchCV → AUC-ROC 0.696 (5-fold CV: 0.677 ± 0.0008)
- Explainability: SHAP TreeExplainer for per-prediction reasoning, not just global feature importance
- Serving: FastAPI + Pydantic validation, 8/8 tests passing, Dockerized, deployed via GitHub Actions CI/CD
- Interface: Streamlit dashboard with a live OpenSky air-traffic overlay
Live Dashboard · API Docs · Source
Python XGBoost SHAP FastAPI Docker GitHub Actions Streamlit
| Project | Description | Stack |
|---|---|---|
| japan-trade-intelligence | Bilateral trade intelligence system for Japan — 6-country comparison, HHI concentration-risk modeling, forecasts validated against real 2024–25 outcomes | Python, Pandas, NumPy |
| mercari-price-analysis | EDA and feature engineering on 50K Mercari marketplace listings — price-distribution and category/brand pricing signals | Pandas, NumPy, Seaborn |
| mercaridb-mysql-30days | Applied MySQL project modeled on Mercari's schema — query design, indexing, normalization | MySQL |
| awesome-japan-tech-blogs | Curated list of engineering blogs from Japan's top tech companies (Mercari, CyberAgent, LINE, DeNA, and more) | Markdown / curation |
Languages: Python, SQL ML / Data: Pandas, NumPy, Scikit-learn, XGBoost, SHAP, Matplotlib, Seaborn Engineering: FastAPI, Docker, GitHub Actions, Streamlit, Git Currently learning: Deep learning fundamentals, LLM fine-tuning, LangChain
Open to ML engineering internships and collaboration — reach out on LinkedIn