AI-Powered Search Transformation
Led the overhaul of a world-leading research lab’s search infrastructure, transforming fragmented data across petabytes of structured and unstructured information into a scalable, API-first platform integrated with 50+ applications.
Challenge
Search capabilities were fragmented across the organization, with siloed data and inconsistent access limiting knowledge discovery and operational efficiency.
Solution
Developed and delivered an API-first, self-service search platform that provided real-time access to petabyte-scale data while ensuring compliance with ITAR and PII regulations.
Impact
Increased search accuracy by 1,320%, reduced data onboarding time from 280 days to 20 days, saved $3M annually, and enabled seamless integration for 50+ applications.
Project Overview
A world-leading research laboratory struggled with fragmented, siloed search services across petabytes of structured and unstructured data, spanning hundreds of data stores. Engineers and researchers faced significant delays in retrieving mission-critical information from numeric datasets, technical reports, scientific papers, telemetry logs, and document archives.
I led the design and deployment of an AI-powered, API-first search platform, enabling seamless search access for researchers, engineers, and leadership. This system integrated machine learning-driven ranking, entity recognition, and a scalable hybrid cloud architecture, ensuring high-performance, secure, and compliant search capabilities.
Engaging stakeholders across all levels, including the C-suite, I secured buy-in and drove adoption. The platform eliminated redundancy, improved accessibility, and ensured real-time insights while meeting ITAR and PII compliance requirements.
The result? Increased search accuracy by 1,320%, reduced data onboarding time from 280 days to 20 days, saved $3M annually, and enabled seamless integration for 50+ applications.
Key Achievements
- Reduced data onboarding time from 280 days to 20 days, ensuring faster access to critical datasets.
- Increased search accuracy by 1,320%, improving knowledge discovery and reducing redundant work.
- Saved $3M annually by optimizing infrastructure, eliminating redundant compute workloads, and improving indexing efficiency.
- Enabled integration for 50+ applications with a centralized, API-first, self-service search platform, eliminating fragmentation and standardizing enterprise-wide data access.
- Secured buy-in from executive leadership and engineering teams, ensuring successful adoption across multiple lines of business.
- Established real-time search capabilities, eliminating indexing delays and enabling instant access to mission-critical data.
Technical Execution
- Designed a hybrid search model integrating full-text (Elasticsearch) and AI-powered semantic search (BERT).
- Developed knowledge graph-based entity recognition, improving contextual search and cross-referencing of scientific data.
- Built an API-first architecture supporting programmatic search access across petabytes of structured and unstructured data.
- Implemented multi-cloud architecture with AWS GovCloud and on-prem Kubernetes for secure, ITAR-compliant workloads.
- Developed real-time Kafka and Airflow-based ETL processes to process and index vast datasets efficiently.
- Established CI/CD pipelines for continuous search relevance testing, ensuring ongoing improvements without regression.
Leadership & Strategy
- Led multiple engineering teams across search, DevOps, and infrastructure, aligning efforts toward a scalable, API-first search platform.
- Engaged C-suite, researchers, and engineers to ensure the system met scientific and operational needs.
- Worked with the Chief Data Officer to establish enterprise-wide data governance policies, ensuring compliance with ITAR, PII regulations, and secure data access practices.
- Improved cross-team collaboration, integrating product, data science, and compliance teams into the development process.