I design machine-learning systems from research intuition through measurable production impact, turning noisy data into reliable predictions, retrieval and intelligent product behavior.
My engineering practice sits between experimentation and dependable systems: clean data contracts, reproducible training, evaluation that reflects real user outcomes, and deployment patterns designed for iteration.
A deliberately practical toolkit spanning modeling, data, evaluation and serving, presented as a system map rather than a generic skill meter.
Build, evaluate and operate ML workflows where model quality and engineering reliability reinforce each other.
Led retrieval and ranking initiatives, established offline/online evaluation loops, and moved experimental models into monitored production services.
Built forecasting and NLP pipelines, automated training workflows, and partnered with product teams to translate ambiguous problems into measurable ML objectives.
Developed predictive models and experimentation frameworks, with a focus on interpretable evaluation and robust feature pipelines.
A production RAG architecture combining semantic retrieval, reranking and grounded response evaluation. Reduced irrelevant retrieval and made failure cases observable across the full inference path.
Probabilistic demand forecasting with drift-aware retraining.
Efficient visual classification pipeline optimized for edge inference.
Clarify the decision, user outcome, constraints and success metric.
Build a defensible baseline, test hypotheses and track meaningful error.
Package inference, data checks, evaluation and observability for real use.
Use production evidence to retrain, refine and improve the system.
For AI product work, ML platform challenges, applied research or engineering collaborations, send a concise brief.
Typical response: 2 business days