PRODUCT · AI BUILDER · CUSTOMER CENTRIC
Product & AI Builder — I ship things that solve real problems and measure whether they worked.
I'm targeting product management, strategy, and AI-forward roles where I can work on hard problems, ship fast, and keep learning.
A small prototype I built based on a Shopify shipping setup assignment prompt. The problem: many merchants abandon setup before finishing when they hit unfamiliar shipping terms. My solution: a guided assistant that asks two plain-English questions and configures the setup flow automatically. Built and deployed as an interactive prototype on Netlify.
Led a multidisciplinary team in designing an AI-powered assistive system supporting visually impaired individuals and dementia patients with spatial awareness and memory reinforcement.
Building an end-to-end LLM + RAG system that dynamically retrieves role-specific knowledge and generates structured interview simulations with feedback.
Engineered and benchmarked PaDiM, SPADE, and CLIP-based models using PyTorch and OpenCV for video anomaly detection.
I identify problems, build solutions, and measure whether they worked. My background spans AI engineering, product delivery, and data analysis — I use all three to ship things that actually move the needle. My work spans prompt engineering, retrieval pipelines, backend APIs, and enterprise-grade deployment.
I enjoy converting ambiguous product and policy constraints into deployable systems that teams can trust.
Enabled reliable, structured AI-assisted case review decisions in the SAMS enterprise case management system by validating model responses for accuracy — engineered using Jinja-based prompt templates for production LLM workflows.
Reduced manual case-processing overhead in a regulated government environment by approximately 79% by building secure RESTful APIs and backend orchestration layers for agentic, AI-assisted decision workflows.
Improved reliability and stakeholder alignment of LLM integrations by defining measurable success metrics and iterating on prompt/retrieval pipeline design — deployed and debugged via Azure DevOps CI/CD.
Improved targeted marketing effectiveness by 15% by translating ambiguous business questions into structured, metric-driven insights — analyzed customer behavior and market segmentation data using Python and Tableau.
Enabled faster, clearer decision-making for cross-functional stakeholders by distilling complex data into actionable recommendations — presented through dynamic dashboards.
Improved data collection efficiency and downstream analysis quality by 20% by designing and deploying customer surveys with optimized question logic.
Streamlined cross-department operations by integrating backend services and automating workflows — implemented and configured using ERPNext modules.
Improved reporting accuracy and operational efficiency by reducing manual processing by approximately 30% — achieved by building automation pipelines.
Featured reads from my Substack on practical AI concepts and real-world thinking.
Your weekly curated reading list — dropping every Friday.
Read on Substack
Your weekly curated reading list — dropping every Friday.
Read on Substack
Your weekly curated reading list — dropping every Friday.
Read on Substack
What's Causing Them, How We Catch Them, and What the Research Says About Fixing Them
Read on Substack
Your weekly curated reading list — dropping every Friday.
Read on Substack
Why AI Confidently Makes Things Up, And Then Agrees With You When You're Wrong Too
Read on SubstackPick role + difficulty, beat the timer, stack a streak, and train like an interview game.
A churn dashboard spikes for one cohort. What is your first 30-minute investigation plan?