Hire Shreya C. - Vector Database Engineer

Built AI-driven backend services using Python, FastAPI, and vector databases to improve restaurant/menu search ranking and personalization, reducing query latency by 40% during peak dinner hours. Developed scalable ingestion pipelines with Pinecone, LangChain, and async processing to embed and index restaurant menus, courier profiles, and marketplace signals, ensuring consistent retrieval accuracy across millions of listings. Optimized caching and model-feature storage layers using Redis and PostgreSQL, enabling faster ETA predictions, courier recommendations, and order-fulfillment decisions while eliminating recurring timeout issues. Deployed LLM and ML model updates through Docker and Kubernetes, powering 100K+ daily inference requests for real-time routing, demand forecasting, and support automation, with zero-downtime rollouts. Monitored latency patterns and throughput using Prometheus and Grafana, identifying bottlenecks affecting delivery-time estimation, batching logic, and courier assignment, and implementing fixes that improved service reliability across regions. Implemented OpenTelemetry-based distributed tracing across logistics and search microservices, improving root-cause analysis for routing delays, caching inconsistencies, and search-ranking regressions—cutting diagnosis time by 35%.

* Built REST API features using Java and Spring Boot, improving request handling logic and reducing repeated processing overhead that frequently slowed internal engineering tools. * Updated CI/CD workflows with Docker and GitHub Actions, shortening deployment cycles by nearly 30% and reducing build inconsistencies during weekly regression and integration processes. * Enhanced internal utilities using TypeScript and Node.js, improving data-processing reliability and enabling smoother coordination for teams depending on shared services for development activities. * Analyzed Datadog logs to isolate a recurring schema issue, applying fixes that lowered error occurrences by 18% during high-volume testing sessions.

Developed supervised learning models using TensorFlow and PyTorch, improving classification accuracy by nearly 20% after refining feature inputs and adjusting training parameters across multiple iterative experiments. Built feature pipelines with Python, Pandas, and NumPy, reducing manual preprocessing time by 45% and enabling faster experimentation cycles for continuing validation studies within the analytics team. Deployed inference workloads using Kubernetes and Docker containers, ensuring predictable scaling during peak traffic and maintaining application response times within acceptable ranges for dependent downstream systems. Monitored model drift through MLflow dashboards to schedule retraining windows effectively, helping maintain stable prediction quality and consistent performance across all deployed production environments. Systematized recurring ingestion tasks using Airflow, lowering operational effort and improving consistency across data pipelines previously affected by manual scheduling issues and intermittent processing interruptions.