I’m Maxwell, a Full-stack Machine Learning Engineer focused on building end-to-end ML systems from data ingestion and pipelines to model training, deployment, and monitoring.
I’m intentionally combining Machine Learning and Data Engineering, because models are only as good as the systems that feed, serve, and scale them.
- End-to-end ML projects (data → features → model → API → monitoring)
- Data pipelines using tools like Spark, dbt, Kafka, and SQL
- Serving ML models via APIs and production-ready workflows
- Advanced Machine Learning Engineering practices
- MLOps (model versioning, CI/CD, monitoring, reproducibility)
- Distributed data processing and scalable systems design
- LLMs
- Full-stack ML systems
- Data engineering for ML
- Model deployment and reliability
- Adversarial & secure ML (offensive and defensive perspectives)
- Real-world ML projects with production constraints
- Data engineering pipelines for analytics or ML
- Open-source projects related to ML infrastructure
- SQL & data modeling
- ML fundamentals and model evaluation
- Designing ML pipelines from scratch
- Bridging data engineering and ML engineering
I care more about whether a model survives production than whether it scores 0.2% higher offline.