Senior Software Engineer - Siemens Technology and Service Private Ltd
(2023-07)
Next-generation HMI runtime platform for Siemens Unified Comfort Panels, enabling reliable visualization, diagnostics, and control in industrial automation environments. Built on modern web-based technologies (HTML5, SVG, JavaScript) and fully integrated with TIA Portal, the runtime delivers scalability from compact panels to large SCADA systems. It ensures robust performance in harsh industrial conditions, supports secure connectivity (OPC UA, VNC, MQTT), and complies with TLS/SSL and OSS security standards, forming the backbone of Siemens' future-proof HMI/SCADA strategy.
- Developed and maintained Alarm Persistency feature ensuring 100% alarm retention during power loss, crashes, and reboots on Unified Panels.
- Led development of VNC connectivity on ARM-based Unified Panels, reducing on-site support by ~40% and enabling secure remote diagnostics with SSL encryption, authentication, and OSS compliance as per European security standards.
- Resolved dual-keyboard conflicts by integrating Qt-DBus communication between taskbar and runtime keyboards, introducing dynamic visibility control and reducing input errors by ~10%.
- Drove Gen AI adoption by leveraging GitHub Copilot, RAG-based productivity aids, and other AI-driven solutions, improving development speed and team productivity by ~35%.
- Resolved 100+ critical bugs, enhancing feature reliability by ~60% and contributing to customer retention, including key accounts such as LG.
- Integrated unit testing for features like Alarm Persistency, improving failure detection and reducing maintenance workload by ~30%.
- Provided performance and environment support, enabling developers to enhance maintainability, optimize workflows, and speed up development.
- Implemented AI-based optimization for NFR reports, improving issue identification during non-functional requirement (NFR) testing.
- Mentored new team members on C++ best practices, coding standards, architectural design, accelerating onboarding and improving code quality.
- Worked in Agile environment with Git and Azure DevOps, ensuring effective collaboration, continuous integration, and streamlined deployments.
Software Engineer - Siemens Technology and Service Private Ltd
(2020-07 - 2023-07)
- Migrated a legacy project to Git and integrated a background worker into the CI/CD pipeline, automating deployments and enabling server-side unit testing to improve reliability and efficiency.
- Contributed to the Alarm Control System by developing a real-time collaboration feature that allows sharing of alarm connections between two devices, and optimized rendering performance on Unified Panels.
- Built and optimized a Debian 11 virtual machine for testing, debugging, and performance profiling of Runtime Software, reducing environment setup time by approximately 50%.
- Utilized advanced C++ frameworks and features (Qt, Boost, Templates) to accelerate development, enhance performance, and improve code reusability.
- Refactored the repository for compatibility with IOWA's updated JFrog libraries, including the removal of FileSys dependencies and deprecated character interfaces/libraries.
- Consistently delivered features on time under tight deadlines while upholding high standards for code quality and testing.
Data-Science Intern - Climate Connect Technologies
(2019-06 - 2020-06)
A renewable energy initiative focused on building a forecasting system for wind and solar power generation. The project aimed to predict weather-dependent energy output in short- and medium-term intervals, enabling power plants and grid operators to optimize scheduling, balance supply and demand, and reduce reliance on non-renewable backup sources. By improving forecast accuracy, the project contributed to better integration of renewable energy into the grid, cost savings, and enhanced sustainability.
- Applied various statistical and machine learning techniques to achieve ~85% accuracy in real-time wind power forecasting.
- Implemented a neural network–based model leveraging historical wind data and current forecasts to predict wind power generation across multiple horizons: 1–3 days, 7–10 days, and up to 1–3 months, based on requirements.
- Performed exploratory data analysis (EDA) to identify patterns, trends, and outliers, and developed automation to select the most accurate ML model among multiple candidates, with real-time accuracy reporting.
- Developed a solar power forecasting module using image analysis of the sun at 30–60 minute intervals to track cloud and rain patterns through image processing, enabling accurate solar generation forecasts for 1–3 days, 7–10 days, and up to 1–3 months, based on requirements.
- Developed a module that evaluates the accuracy of all models running for power generation forecasting, displays the results on the website, and automatically switches to the model with the highest accuracy based on historical data.
