● Developed and deployed a chatbot leveraging OpenAI LLM APIs for contextual assistance using structured CSV datasets, integrating Node.js to automate data ingestion and enhance user interactions. ● Leveraged LangChain to build a full-stack chatbot pipeline including document loaders, memory, custom prompt chains and response generation logic. ● Used ChromaDB as the vector store for fast and relevant similarity search, enabling the chatbot to retrieve context-aware responses. ● Deployed the chatbot for customer-facing use, allowing end users to interact with devices through conversational interfaces for sensor configuration, OTA updates and device restart/reboot operations, enhancing user autonomy and reducing support overhead. ● Achieved full-stack logging and error transparency across all codebases and AWS service layers using AWS X-Ray, significantly improving productivity in defect resolution cycles. ● Added animal tracking and interaction APIs using Node.js and GraphQL, enabling accurate movement detection for over 90% of tracked animals. ● Designed and implemented interactive charts and graphical widgets to visualize sensor data (Hydrostatic, Temperature, Wind and Humidity) with 99% mathematical accuracy. ● Built custom applications to monitor complex agricultural scenarios, including Hay Storage monitoring and Farm Spray advisory systems, delivering actionable insights with 90% accuracy across varying weather conditions. ● Deployed internal monitoring modules allowing customer support teams to track signal, battery and telemetry data of deployed IoT devices with 100% reliability. ● Resolved multiple production-level issues, including device telemetry errors, data visualization bugs and performance bottlenecks in agricultural monitoring platforms. ● Applied improved caching strategies and indexing techniques, accelerating live data accessibility and elevating user experience for farm monitoring applications. ● Integrated robust monitoring and alerting systems using AWS CloudWatch and SNS, enabling real-time detection and escalation of IoT anomalies and service disruptions.

● Fixed critical bugs for a DTH customer service app and implemented a package monitoring module using Node.js, enabling timely delivery of updated DTH package announcements to users. ● Implemented a supply chain tracking module to enhance transparency for customers, warehouse, and management personnel using cloud-based dashboards. ● Developed core features for a banking app using Ionic 4, facilitating secure and seamless currency transfers while ensuring responsiveness across multiple devices. ● Migrated legacy ETL processes for a banking client to a modern cloud-based architecture using Node.js and Azure Functions, making them easier to manage and adapt to variable data arrival times. ● Transferred multilingual banking funds data (1200+ records spanning 15 years) to a cloud-based SQL datastore using custom Python scripts. ● Refined deployment workflows by integrating automated testing and CI/CD pipelines via Azure DevOps, increasing release reliability for enterprise systems. ● Designed an integration module to unify disparate customer interaction logs into a single dashboard, streamlining access for support, sales, and analytics teams. ● Engineered an audit trail framework to monitor system changes and access across distributed platforms, ensuring compliance and improving security posture. ● Developed and integrated RESTful APIs across multiple enterprise projects using Python and Node.js, enabling real-time data sync and reducing incident resolution time by 35%.