Building AI that works
in production
With over a decade of experience in software engineering and data science, I help organizations bridge the gap between AI research and real-world deployment. I specialize in designing systems that are reliable, scalable, and genuinely useful — not just impressive demos.
My work spans the full stack: from defining architecture and selecting models to building the data pipelines and observability tooling that keep production systems healthy. I have led teams across enterprise and startup environments and know what it takes to bring stakeholders and engineers onto the same page.
I am particularly interested in agentic workflows, retrieval-augmented generation, and the emerging MCP ecosystem. If you are working on something hard, I would love to hear about it.
Applied AI & LLMs
RAG pipelines, fine-tuning, prompt engineering, and evaluation frameworks for production LLM systems.
Agentic Systems
Multi-agent orchestration, tool-use, MCP servers, and robust agentic workflows that run reliably.
Data Platforms
End-to-end data infrastructure — ingestion, transformation, warehousing, and real-time streaming.
Enterprise Adoption
Guiding organizations through AI strategy, change management, and responsible deployment at scale.
Cloud Architecture
Designing resilient, cost-efficient cloud-native systems on AWS, GCP, and Azure.
Technical Leadership
Mentoring engineering teams, defining technical roadmaps, and driving quality across organizations.
Open Source Work
A selection of tools and experiments I have built and open-sourced.