I'm a Machine Learning / AI Engineer based in Toronto, Canada, focused on taking models from research to production – especially in search, retrieval, and personalization systems. I care about clean infrastructure, observability, and building systems that are reliable and maintainable.
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🔭 Currently working on:
- Two products that hopefully can be useful for a lot of people
- Search and retrieval systems that surface the right content at the right time
- Personalized recommendation systems (e.g., two-tower architectures) from training → deployment → monitoring
- ML infrastructure for reliable model serving and evaluation
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🌱 Learning more about:
- Kubernetes, scalable inference, and production observability
- LLM applications for search, ranking, and summarization
- How to turn side projects into real, revenue-generating products
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🎯 Longer-term direction:
- Exploring what it takes to run a lean, one-person ML/search practice, from idea → prototype → paying users
- 🔎 Search and ranking: query understanding, retrieval, and learning-to-rank
- 🎯 Personalization: embeddings, two-tower models, and user–item interaction modeling
- 🧩 ML infra: deployment, orchestration, observability, and evaluation loops
- 🤖 LLM-powered products: retrieval-augmented generation and agent-like workflows that plug into production systems
I’m open to:
- 🧭 Helping teams improve or build search and personalization systems end to end
- ⚙️ Turning existing ML prototypes into production services
- 🌐 Collaborating on open-source tooling for ML deployment and monitoring
If you’re working on search, personalization, or ML infra, I’d love to chat.
