Full-Stack → ML Engineer
I build end-to-end systems and now focus on applied machine learning and production ML.
4 ML ProjectsMetrics-drivenProduction mindsetPython / PyTorch / SklearnDeployment experience
Transition Story
- Background as a Full-Stack Engineer
- Why ML (impact + interest)
- How I'm proving it (projects, evaluation, systems thinking)
Featured Projects
End-to-end machine learning projects showcasing practical skills and production mindset.
Customer Churn Prediction
tabularchurnproduction
Predicting which customers are likely to churn for a SaaS platform.
Approach:Baseline: Logistic Regression → XGBoost → Feature selection → SHAP analysis.
Key Metric:AUC: 0.89, Precision@Top10%: 0.72
NLP Support Ticket Routing
nlpclassificationbert
Automated routing of support tickets using NLP classification.
Approach:Baseline: TF-IDF + Logistic Regression → fine-tuned BERT → error analysis.
Key Metric:Macro F1: 0.81, Routing accuracy: 92%
Energy Demand Forecasting
time-seriesforecastinglstm
Forecasting hourly energy demand for a utility provider.
Approach:Baseline: ARIMA → LSTM → Feature engineering → Hyperparameter tuning.
Key Metric:MAE: 0.13, RMSE: 0.21
RAG-powered Knowledge Base
ragllmretrieval
Retrieval-Augmented Generation for internal knowledge base Q&A.
Approach:Baseline: BM25 → Dense retrieval → OpenAI GPT-3.5 → RAG pipeline.
Key Metric:Top-1 accuracy: 84%, Mean reciprocal rank: 0.78
Skills Snapshot
Machine Learning
- Supervised & Unsupervised Learning
- NLP (BERT, Transformers)
- Time Series Forecasting
- Model Evaluation & Metrics
- Feature Engineering
- Error Analysis
MLOps / Production ML
- Model Deployment (Docker, FastAPI)
- Experiment Tracking
- Reproducibility
- Monitoring & Logging
- CI/CD for ML
- Cloud (AWS, GCP)
Software Engineering
- System Design
- API Development
- Frontend & Backend
- Testing & Quality
- DevOps & Automation
- Agile Delivery
Proof of Seriousness
- Baselines → improvements
- Metrics selection
- Error analysis
- Reproducibility
- Monitoring mindset
Recruiter Quick Scan
Roles Targeted
- Machine Learning Engineer
- Applied Scientist
- Full-Stack ML Engineer
Strengths
- Production ML systems
- End-to-end delivery
- Metrics-driven approach
- Strong engineering background
Looking For
- Impactful ML roles
- Teams with strong engineering culture
- Opportunities to ship ML to production
Let's Connect
Interested in discussing ML opportunities or collaboration? I'd love to hear from you.