Lou – LLM, LangChain, RAG

A shared, channel-agnostic AI platform that serves as the core intelligence layer across multiple communication channels and product domains. The system was designed to process text-based interactions independently of the delivery channel, enabling consistent conversational logic, context management, and business workflows while supporting reuse across different products and use cases.

• Unified existing channel-specific AI implementations into a shared, channel-agnostic core, enabling the same underlying engine to be reused consistently across multiple communication channels and product verticals;
• Led the design of a decoupled conversational intelligence architecture, separating AI logic from channel-specific concerns to improve scalability, maintainability, and reliability;
• Designed and implemented conversation scoring, evaluation frameworks, and quality metrics, making conversational performance measurable across AI-powered products;
• Worked at the intersection of AI architecture, engineering, and product strategy, helping define how conversational AI systems were built, evaluated, and scaled across the organization;
• Served as a technical reference for AI systems design, platform extensibility, and long-term architectural direction.

An AI-powered conversational platform that automated customer interactions for the residential leasing process via SMS. The system handled inquiries, guided prospective tenants through the rental journey, answered property-related questions, and supported lead qualification using large language models and conversational AI workflows.

• Led a from-scratch rewrite of the AI engine powering Zuma’s leasing assistant over SMS;
• Designed and implemented an agentic system composed of tool-calling agents, specialized sub-agents, and an internal knowledge base;
• Significantly improved conversational quality, reliability, and extensibility, enabling faster iteration and safer evolution of AI behavior;
• Increased conversation success and conversion rates by improving intent handling, reasoning flow, and response consistency;
• Established architectural foundations that later enabled reuse across channels and products.

An in-house RAG system that let users upload text-based documents and chat with them from pre-defined seed prompts.

• Built a proof of concept for an internal RAG system enabling users to upload text-based documents and interact with them through chat interfaces using predefined seed prompts;
• Implemented document metadata storage in MySQL to support indexing, retrieval, and system-level organization of uploaded content;
• Used Amazon S3 as the primary storage layer for document text data, ensuring scalable and durable object storage;
• Integrated Qdrant as a vector database for storing and querying embeddings to enable semantic retrieval functionality;
• Leveraged OpenAI models for both embedding generation and natural language response synthesis within the RAG pipeline;
• Developed a Python-based FastAPI backend exposing ingestion and retrieval endpoints for document processing and conversational querying.

A project health analytics and contributor intelligence product across 15,000+ open-source repos.

• Owned and delivered department-wide initiatives, providing regular progress updates to senior leadership;
• Designed and monitored detailed project schedules, assigning responsibilities across multiple teams;
• Led proposal development efforts, presenting technical opportunities for go/no-go decisions at the leadership level;
• Directed a cross-functional team of approximately 15 members, including engineering, QA, and DevOps, spread across the Americas to Asia-Pacific time zones;
• Collaborated closely with Product leadership to align on priorities, roadmap execution, and long-term strategy;
• Established efficient task assignment frameworks, reducing redundancy and minimizing interdependencies;
• Attended weekly department head meetings to communicate updates, surface risks, and ensure cross-team alignment;
• Worked directly with vendors and internal sourcing teams to explore new opportunities, optimize solutions, and identify cost-saving measures.