I’m a PhD-trained machine learning practitioner with 20+ years in software engineering and leadership, currently working at a healthcare AI company.

I specialize in building production-grade AI systems: from classical ML to LLM-based architectures—that operate in real-world, high-stakes environments such as regulated healthcare and large-scale consumer platforms. My work spans model design, system architecture, MLOps, robustness testing, and deployment, with a strong focus on reliability, safety, and measurable business impact.

I’ve led AI initiatives that increased revenue, reduced operational costs, and replaced manual workflows with scalable machine-learning systems. I regularly work with executives and engineering teams to bridge the gap between AI theory, product requirements, and production realities.

My talks focus on what actually works in production AI - common failure modes, architectural trade-offs, and practical patterns for building high-quality, high-impact AI systems that teams can trust.