TANMAY AGRAWAL

• Spearheaded development of a customer onboarding automation flow, reducing onboarding time by ~66% and accelerating client go-live speed.
• Led the planning and implementation of a project from scratch - "Automated PDP Generation". Orchestrated things from inception to completion – from writing the tech spec, architecting scalable Temporal.io workflows, distributing tasks, setting CI/CD pipeline, to managing the Kubernetes (GKE) deployments.
• Achieved ~70% reduction in client onboarding time and ~50% bandwidth increase for product operations team via Automated PDP Generation initiatives.
• Built product clustering system using Agglomerative Hierarchical Clustering (AHC) to process large-scale JSON datasets and automatically group products with similar features, resulting in optimized storefront UX.
• Drove ~50% increase in conversion rates in 3 months by collaborating on designing and deploying new experiment-driven shopping experiences.
• Developed high-performance retail consumer-facing components using Next.js, Tailwind CSS, and architected the dashboard platform modules with React, Chakra UI, and Apollo GraphQL.
• Designed and deployed scalable GraphQL APIs with Hasura; implemented mission-critical backend services (checkout, cart validation, product-info) using Golang.
• Led a self-serve signup and payment workflow project, integrating Stripe and Auth0 to implement identity-aware access controls, permission-based account state transitions, and PCI-compliant payment processing.
• Contributed to the development of Pierre, the Al agent for platform, integrating OpenAl and Anthropic APIs and optimizing prompt engineering for faster, high-fidelity LLM responses.
• Improved platform reliability and developer workflow efficiency by automating production DB migrations with CI/CD pipelines using Flyway & GCP Build trigger.

• Engineered a real-time radar data ingestion pipeline with synchronized frequency control, achieving consistent 100ms output periodicity across C-based embedded firmware and Python-based desktop app
• Built a low-latency, multithreaded system to decode high-frequency binary device streams and render real-time visualizations for radar point clouds.
• Architected a radar GUI application using wxPython for dynamic analysis and visualization of 3D sensor data streams in live environments.
• Developed a presence detection algorithm by applying DBSCAN clustering on point-cloud data with Scikit-learn, enhancing device intelligence.
• Contributed to research and prototyping of object classification strategies, improving radar system capabilities for future automotive and loT products.
• Prototyped classification models using PyTorch

• Built REST-based integrations with third-party apps (Google Drive, Jira, Slack), enabling data ingestion pipelines for LLM-based augmentation.
• Integrated OpenSearch to store vector embeddings.
• Designed and deployed event-driven Temporal.io workflows triggered by MySQL CDC events, orchestrating real-time file/text extraction and semantic indexing via Llamalndex.
• Engineered dynamic search and filtering components in React using Tailwind CSS, improving UX responsiveness for large document datasets.
• Optimized indexing throughput via API request parallelization, achieving 50x performance gain

Thesis under Dr. Palem's supervision on a project to bring the 'Quantum Advantage' sooner