Semantic Search for website and runbook catalog

• Designed and applied four comprehensive GPT prompt-based test metrics to assess search results and embedding models

• Evaluated embedding models(Ada, Multiqa), chunked and embedded runbooks into FAISS and sqlite databases using langchain

• Established a Git workflow for automated embedding files generation and Docker image creation for deployment

• Implemented a responsive search endpoint for 800 runbooks with associated commands, achieving 500 ms response time

• Developed server-specific custom search index workflow, allowing users to perform searches on their private runbooks using an optimistic concurrency control approach, caching with S3 storage and Redis background workers

• Mentored and supervised two interns through onboarding, collaborating to successfully launch search endpoint on production

End-to-end data pipelines for ticketing tools(Pagerduty, Opsgenie, Servicenow)

• Created and configured multiple Airbyte connectors(TypeScript) and data streaming scripts for data extraction, transformation, daily synchronization, and loading into PostgreSQL, providing user with near-instance data access

• Unified diverse data sources schema and migrated 14000 tables using Flask and SQLAlchemy, optimizing codebase clarity • Implemented interactive UI data visualization reports(React.js), transforming backend SQL queries into filter-enabled graphs • Enabled runbook generation utilizing GPT-3.5 and prompt techniques to auto-create Generative AI incident remediation

• Designed and implemented data preprocessing and modeling pipeline using AWS SageMaker for dynamically chute mapping update on sortation floor to replace expensive runtime simulator

• Embedded high dimensional destination distribution data with station spatial location into multi-channel tensors and scatter images with aggregation to extract floor information, built surrogate models with Multi-layer Perceptron that improved 28.8% MAE and 0.527 R2 from Baseline to predict package throughput evaluation metrics

• Compared approaches performance with PCA reduced Linear Regression, MLP, CNN and pretrained networks, interpreted features measures floor congestion, and developed recommendations for further modeling improvement

Analyzed the website data impact and visualized into dashboard by using web scrapping and Google Analytic tools

Co-lead of the FairVision Cam2 subteam. Utilizing Generative Adversarial Network(GAN) to remove image dataset biases in the training data of image recognition and classification algorithms.