About Me

I'm a Partner Solutions Architect specializing in Artificial Intelligence & Data Platforms at MongoDB, helping enterprises and partners move from proof of concept to production by designing scalable, secure, and responsibly governed AI systems.

With nearly 10 years of experience in enterprise solution architecture and partner integrations, I've recently focused on designing, deploying, and scaling Generative AI solutions. My work spans Systems Integrators, cloud partners, and startups, enabling them to transition from PoV to production through one-click reference architectures, reproducible deployment pipelines, and partner enablement at scale.

In the past year, I've architected more than 20 reference solutions and open-source accelerators integrating MongoDB Atlas with Anthropic Claude, OpenAI GPT-4, Vertex AI, and AWS Bedrock—reducing partner setup time by ~80% and standardizing deployment patterns for RAG, semantic caching, and multi-agent orchestration.

My hands-on experience includes integrating LLMs (GPT, Claude, Llama, Mistral), vector search, and multi-agent systems with converged datastores (MongoDB Atlas) and cloud infrastructure (AWS, GCP, Azure). I'm strong at pre-sales discovery, solution design workshops, post-deployment support, and translating partner feedback into product outcomes. I serve as an experienced "user-zero" for model testing, SDK validation, and cloud deployment automation across AI workloads.

Before MongoDB, my work spanned DevOps and cloud modernization initiatives that collectively delivered $3.2M in documented customer value through improved efficiency, automation, and data reliability—a foundation that now enables me to design AI architectures that scale reliably in production.