Derrek Williams

Architected a secure enterprise RAG platform using Python, FastAPI, Pinecone, PostgreSQL, and OpenAI/Anthropic models, cutting average product-information research time by ~48% across merchandising, marketing, and support. Built hybrid retrieval and reranking pipelines combining semantic search, keyword matching, metadata filters, and source prioritization to improve top-result relevance by ~31% and reduce unsuccessful searches by ~42%. Implemented strong security with row-level security, server-side authorization, tenant-aware filtering, and audit logging across 100% of document requests to maintain zero cross-domain exposure incidents. Orchestrated asynchronous agent workflows connecting product data, CMS platforms, and approval systems—reducing campaign content-prep time by ~58% and shortening review cycles by ~34% while preserving human publishing controls. Established automated LLM evaluation pipelines (retrieval relevance, citation accuracy, groundedness, latency, and structur

Developed a multi-tenant semantic search SaaS using Python, FastAPI, Pinecone, PostgreSQL, and OpenAI embeddings, improving relevant-result selection by ~27% and reducing zero-result searches by ~33%. Built reusable ingestion connectors for CMS, CRM, document repositories, and third-party APIs, reducing average client onboarding time by ~45% across multiple engagements. Implemented tenant-specific vector namespaces, PostgreSQL row-level security, and server-side authorization for multiple client environments to ensure complete data separation with zero cross-client access incidents. Productized an AI-assisted content workflow that generated and summarized customer-facing material from CRM and CMS data, reducing first-draft preparation time by ~52% and increasing weekly content throughput by ~36%. Standardized prompt templates, structured outputs, validation rules, caching, and API retry handling to reduce inconsistent model responses by ~30% and lower AI-related support issues by ~21%

Designed an event-driven analytics architecture on Google Cloud using Pub/Sub, Cloud Functions, and BigQuery to process 8M+ application events per day while decoupling producers from downstream analytics consumers. Built near-real-time ingestion and transformation pipelines, reducing analytics data availability from ~3 hours to under ~12 minutes for supported product datasets. Hardened distributed processing by implementing idempotent consumers, schema validation, dead-letter handling, replay workflows, and automated alerting, improving successful event-processing rate to ~99.6% and reducing manual recovery effort by ~57%. Streamlined BigQuery table partitioning, clustering, and transformation logic to lower targeted query costs by ~31% and improve average analytical query execution times by ~37%. Instrumented A/B testing pipelines with assignment validation, data freshness monitoring, and outcome reconciliation, cutting experiment-data preparation time by ~44% and identifying broken