Strategic Investment Professional transitioning to AI Engineering - Building practical AI tools that solve real problems ✨

Strategic technology leader with a unique blend of Investment/VC background and cutting-edge AI transition. I transform complex financial data into actionable insights and build AI systems that bridge traditional finance with emerging tech.

Investment & Strategic Background:

• 💰 Venture Capital, Private Equity, Family Office Operations
• 🏢 Real Estate Investment, Institutional Capital, Cross-border Deals
• 📈 Strategic Planning, Go-to-Market, International Expansion
• 🎌 Japanese Language & Startup Operations Leadership
• 📊 Investment Analysis, Portfolio Management, Due Diligence
• 💼 Financial Modeling, Data Analysis, M&A Experience

Give Claude ChatGPT-style memory - Local RAG system for searching conversation history

• 🎯 The Problem: Claude forgets everything between conversations
• ✨ The Solution: Semantic search through your chat exports using ChromaDB + LangChain

Data Engineer + AI Agent Specialist | Building production systems that bridge business strategy and technical execution ⚡

I combine 20 years of VC/Finance leadership with 5 years of intensive AI/ML engineering to build data infrastructure and intelligent automation systems. I don't just build pipelines—I architect solutions that executives understand and engineers respect.

What Makes Me Different:

• 💼 Business-First Thinking: 20 years evaluating startups taught me to prioritize ROI over cool tech
• 🔧 Hands-On Execution: Built production data pipelines (BigQuery, dbt) and AI agents (LangGraph, RAG)
• 🌉 Bridge Builder: Translate executive requirements into scalable technical architectures
• 📊 Measurable Impact: 60% faster queries, 80% time savings, 99.5% uptime—I deliver metrics that matter

Production-grade data engineering on Google Cloud - The foundation of modern data infrastructure

The Problem: Companies drowning in data from multiple sources (APIs, CSVs, databases) with no unified view of business KPIs

The Solution: End-to-end data pipeline that transforms raw data into executive-ready dashboards

Key Achievements:

• ⚡ 60% faster queries through optimized SQL (materialized views, partitioning, clustering)
• 📊 Automated KPI dashboards updating real-time from BigQuery (revenue, growth, customer AOV)
• 🚀 500K+ records/day processed with 99.5% pipeline reliability
• 🎯 ML-ready data models for Vertex AI integration
• 🔄 Full CI/CD via GitHub Actions (automated testing, deployment on merge)

Tech Stack: BigQuery • dbt • Vertex AI • Cloud Run • React • Visx • Python • SQL

Business Impact: Enabled data-driven decision making for non-technical stakeholders. Executives now have self-service access to real-time business metrics.

Autonomous AI agents that actually work - Not just chat, but real workflow automation

The Problem: Marketing teams spend 4+ hours weekly creating competitor analysis reports—manually scraping websites, comparing data, writing summaries

The Solution: Autonomous multi-agent system that orchestrates specialized AI workers to complete complex workflows

Architecture:

• 🔬 Research Agent: Web scraping + multimodal doc analysis (Qwen2-VL for images, Llama-3.2-Vision for PDFs)
• 📊 Data Agent: SQL queries + RAG retrieval from internal databases
• ✍️ Writer Agent: Natural language report generation with formatting
• 🎯 Reviewer Agent: Quality checks + fact verification before final delivery

Key Achievements:

• 🤖 Full autonomous workflow using LangGraph state machines (no hardcoded logic)
• 🧠 Multimodal RAG pipeline combining text + image embeddings (FAISS/Pinecone)
• ⚡ 80% time reduction (4 hours → 30 minutes for weekly reports)
• 🔧 Tool-calling agents that autonomously select and execute actions (Google Search, SQL, APIs, calculators)
• 🎛️ Real-time monitoring via Streamlit dashboard (watch agents collaborate live)

Tech Stack: LangGraph • LangChain • Qwen2-VL • Llama-3.2-Vision • FAISS • Pinecone • FastAPI • Streamlit • Docker

Business Impact: Marketing teams now focus on strategy instead of data collection. Competitor intelligence delivered consistently every Monday morning.

Give Claude ChatGPT-style memory - Semantic search through conversation history

The Problem: Claude forgets everything between conversations—no way to reference past discussions

The Solution: Local RAG system for searching your chat exports using semantic embeddings

Tech Stack: Python • LangChain • ChromaDB • Streamlit • HuggingFace Embeddings

Impact: Instant recall of any past conversation. Ask "What did we discuss about data pipelines last month?" and get relevant excerpts.

Model Context Protocol servers & tools - Enhanced Claude capabilities through filesystem, GitHub, and memory integrations

Tech Stack: MCP Protocol • Python • Claude Desktop

Impact: Turn Claude into a development assistant that can read files, commit to GitHub, and remember project context.

• 💰 Venture Capital & Private Equity: Led $500M+ AUM across 100+ startup evaluations
• 🎯 Due Diligence: Evaluated data infrastructure, SaaS, and AI/ML companies
• 📊 Financial Modeling: Built 5-year projections for portfolio companies
• 🌏 Cross-border Deals: Japanese language proficiency + international expansion strategy
• 🏢 Real Estate Investment: Institutional capital deployment and asset management

Why This Matters for Engineering:

• Understand ROI and prioritize features by business impact
• Communicate technical concepts to C-level executives
• Validate market demand before building (learned from evaluating 100+ startups)
• Translate complex systems into business value for non-technical stakeholders

• 🎓 MBA | University of Chicago Booth School of Business
  • Concentration: Finance, Strategy

• 📊 Meta Data Analytics Certificate (2025)
• ☁️ AWS Cloud Essentials+
• 🧠 Deep Learning Specialization (Coursera/Stanford)
• 🔗 LangChain & LangGraph (production projects)
• 🔄 Scrum Certified (2012)
• 🗾 Japanese Language Proficiency

• 💻 2,000+ hours hands-on coding (Python, SQL, React, FastAPI)
• 🏆 Kaggle Competitions: Active ML competition participant
• 📚 Coursework: Stanford CS229 (ML), MIT 6.S191 (Deep Learning), Fast.ai

🎯 Building production data pipelines and AI agent systems for clients

• Cocktailverse: GCP data engineering (BigQuery + dbt + Vertex AI)
• Coffeeverse: Multi-agent AI automation (LangGraph + RAG + Multimodal LLMs)

⚡ Specialization:

• RAG systems for knowledge management and workflow automation
• Multi-agent orchestration with LangChain/LangGraph
• Multimodal AI (combining text, images, documents)
• Production-grade data pipelines with business impact

🌉 Unique Value:

• Bridge between business strategy and technical execution
• Translate executive requirements into scalable architectures
• Deliver measurable ROI (not just cool demos)

• 🧠 Advanced LangGraph patterns for complex agent workflows
• 🎨 Multimodal embeddings (CLIP, Qwen2-VL, Llama-3.2-Vision)
• ☁️ Google Cloud Professional Data Engineer certification
• 🔧 Production deployment patterns for AI agents

✅ Data Engineering: BigQuery pipelines, dbt transformations, dashboard automation
✅ AI Agents: LangGraph workflows, RAG systems, multimodal AI
✅ Business + Tech Translation: Turn executive vision into technical roadmaps
✅ ROI-Driven Solutions: Not just cool tech—measurable business impact

🧠 Building systems that executives understand and engineers respect

"Most data engineers understand pipelines. Most AI engineers understand models. Few understand both—and even fewer can explain ROI to a CFO. I do all three."

⚡ Quality over vanity metrics | 🎯 Production over prototypes | 💼 Business impact over cool demos