• Trained and deployed a production-grade machine learning model for temporal event detection for Walmart’s IoT infrastructure, enabling in-store item movement tracking with 99.5% accuracy.

• Engineered scalable real-time machine learning pipelines using PySpark and Databricks to process data from over 9M IoT sensors with less than 2 second latency enabling instant analytics.

• Prototyped LLM-based tools using LangChain and OpenAI APIs to enable natural language querying of sensor data. Implemented a RAG memory-layer with vector similarity search to get the most relevant context.

• Executed end-to-end feature engineering on multivariate time series data using NumPy, Pandas, and SQL— partnered with business teams to validate data patterns via hypothesis testing and statistical inference.

• Designed visualizations and analytics dashboards to monitor model performance, drifts and coverage — enabling stakeholders to make data-driven decisions across operations and engineering.

• Engineered a multi-lingual code snippet generation tool using Python that supported 13+ programming languages, reducing snippet generation time by 70% and eliminating 150+ hours of boilerplate setup.

• Built a content standardization pipeline leveraging text parsing, schema validation, and automated formatting, unifying diverse vendor inputs into a consistent, production-ready format.

• Engineered a Python-based automation framework to auto-generate and validate unit tests for iMocha’s question library, scaling test coverage across 1,000+ problems and cutting QA cycles by 80%.