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I build AI products.

I take AI products from idea to shipped, and I do the whole loop myself: product strategy, the build, the launch, the iteration after. Counsel and Verdict are live. I'm currently building product for VidaSana Wellness.

The depth underneath it: three years with the Microsoft Power BI team at LTI Mindtree, shipping enterprise data systems for Boeing, Volvo, HP, and other Fortune 500 clients. MS in Business Analytics, UC San Diego (December 2025). Based in Ashburn, Virginia, open to roles anywhere in the US.

The arc.

2026 — Present
AI Product Builder
Shipping a new AI product roughly every week, each in a different industry. Counsel (fintech) and Verdict (marketing) are live. Building product for VidaSana Wellness. Each product is mine end to end: strategy, build, launch, iterate.
2026 — Present
AI Product Builder · VidaSana Wellness
Building AI product for a bilingual wellness marketplace. Product direction and the build itself, end to end.
Dec 2025
MS Business Analytics, UC San Diego
Energy analytics capstone won Best Project Award — 7% reduction in operational costs across 50+ facilities.
2021 — 2024
BI Developer, LTI Mindtree (Microsoft Power BI team)
Three years embedded in Microsoft's Power BI organization. Delivered enterprise data systems for Fortune 500 clients including Boeing, Volvo, and HP. The engineering depth behind products that actually ship.
2018 — 2022
B.E. Electronics & Communication, Visvesvaraya Technological University
Engineering foundation.

The stack.

How I build AI products: the AI and product layer is where I work now, on a foundation of real production engineering. Not prototypes, things that ship.

01
AI & Product
What I build now
Product, end to end
CORE
Problem framing, PRD, scope calls, the build itself, launch, iteration. Counsel and Verdict are live proof: shipped, not slideware.
LLM Product Systems
CORE
Anthropic Claude API, structured reasoning, output validation, retry logic. LLM features that hold up in production, not just demos.
Prompt Engineering & Evals
CORE
Structured outputs, deterministic guardrails, planted-truth evals. How I make an AI product trustworthy enough to ship.
Agentic AI / MCP
CORE
Multi-step agents that compose external tools via MCP. Architecture-level thinking on how an agent calls search, enrichment, and data.
02
Engineering
The build muscle behind the product
Python
PROFICIENT
Pandas, NumPy, scikit-learn for modeling. Automation scripts, data transformations, API orchestration.
Azure Data Factory
EXPERT
ETL orchestration across cloud sources. Production pipelines feeding Power BI semantic models.
PySpark / Airflow
PROFICIENT
Distributed data processing, scheduled DAGs, dependency resolution. Production ETL at scale.
Apollo / Apify
PROFICIENT
Verified contact enrichment via Apollo MCP, LinkedIn scraping via Apify actors. Data acquisition for B2B-grade workflows.
03
Data & BI
Three years of Fortune 500 depth
Power BI / DAX
EXPERT
Enterprise dashboards, complex measures, model optimization. Microsoft Power BI team at LTI Mindtree, Fortune 500 deployments.
SQL Server
EXPERT
Stored procedures, query optimization, execution plan analysis. Production systems at Boeing, Volvo, HP scale.
Snowflake / Azure Data Factory
PROFICIENT
Cloud warehousing and ETL orchestration. Production pipelines feeding enterprise semantic models.
Python
PROFICIENT
Pandas, NumPy, scikit-learn. Automation, data transformation, the glue under every product I ship.

How I work.

My approach is deliberately deliverable-first. I'd rather ship the smallest real version of a product than write a slide about what could theoretically be built. Counsel and Verdict are the proof: each one a full product, taken from blank page to live and interactive, owned end to end. Same posture for product work, client work, and my own ideas.

Get in touch.

If you're hiring for AI product roles, someone who can own strategy and build the thing, or you're a founder who needs the first real version of your product shipped, let's talk.

Email meLinkedIn