Scientist turned software engineer empowering scientists with high-quality software and scalable infrastructure.

I’m a scientist-turned-software engineer with a strong foundation in AI, machine learning, and data infrastructure. My journey began in bioengineering, where I used computational modeling and cheminformatics to design and validate biological systems. Since then, I’ve built and deployed production-ready ML pipelines at LanzaTech, including Retrieval-Augmented Generation (RAG) systems for internal document search and predictive models for genomics and metabolic data. I specialize in bridging experimental biology with scalable software—working fluently in Python, AWS, FastAPI, and tools like Scikit-learn, Haystack, and RDKit. I’m especially passionate about making complex ML systems secure, interpretable, and useful in real-world scientific and operational settings. In my next role, I’m looking to deepen my work in AI-driven research platforms or ML infrastructure—ideally where I can collaborate closely with scientists and engineers to unlock new insights from complex datasets.