ML Engineering
I build ML features that solve real problems in production. From data pipelines to inference optimization, I help teams move from research to revenue.
8+
years building ML systems
15+
industries deployed
100M+
inferences in production
Practical ML challenges across the product lifecycle
Transform raw data into signals that drive model performance. I design pipelines that handle real-world complexity—missing values, drift, and latency constraints.
Reduce inference latency and memory footprint without sacrificing accuracy. Quantization, distillation, and serving architecture that runs on your infrastructure.
Catch model degradation before it hits users. Build observability into your ML stack—data validation, performance tracking, and automated alerts.
Personalization engines that learn from user behavior. Collaborative filtering, content-based ranking, and real-time feedback loops that improve over time.
Accurate models for high-stakes decisions. Fraud detection, content moderation, anomaly detection—with calibration and explainability built in.
Predict demand, traffic, and trends with models that adapt to seasonal patterns and external signals. From ARIMA to neural networks, the right approach for your data.
Engineering ML systems that work
I work with teams building products in fintech, e-commerce, healthcare, and logistics. My focus is on the gap between research and production—taking models from notebooks into systems that scale, serve millions of requests, and improve with feedback. I've helped teams reduce inference costs by , cut model training time in half, and ship features that users actually rely on. I approach each project as a partnership: understanding your constraints, your data, and your timeline, then delivering solutions that fit your stack and your business.
Let's talk about your challenge. I'll help you scope the work, estimate effort, and plan a path to production.
Reach out to discuss your ML needs or book a scoping session.