Meghana Badhe
Professional Summary
IoT Software Engineer with 0 years of experience building embedded systems, sensor networks, and cloud-connected monitoring solutions. Strong foundation in Python, C, SQL, data analysis, and IoT frameworks (NodeMCU, LoRa-WAN, Blynk). Seeking an entry-level software role to apply hands-on IoT project experience, collaborate with cross-functional teams, and deliver reliable field-ready solutions.
Technical Skills
Work Experience
- Prototyped sensor-driven IoT modules using NodeMCU and environmental sensors; developed embedded C routines to read sensor data and validate sampling intervals for lab demonstrations.
- Applied real-time data collection techniques to stream and log telemetry from prototypes; built Python scripts to parse serial data and generate structured logs for analysis.
- Performed sensor calibration and signal-conditioning experiments to improve measurement reliability; documented procedures and recommended component selections for demonstration rigs.
- Created concise visualizations and exploratory analyses in Excel and Jupyter Notebook to present collected telemetry to supervisors, enabling quicker iteration on prototype design.
- Studied automation and industrial IoT patterns during hands-on exercises, evaluating trade-offs in connectivity, latency, and power for field-deployable solutions.
- Compiled a technical report detailing sensor interfaces, data acquisition methods, test results, and next-step recommendations for integrating prototypes into campus pilot trials.
Projects
- Designed and built flood-monitoring sensor nodes using NodeMCU, water-level sensors, and GPS modules to capture location-stamped telemetry for early flood detection.
- Implemented long-range telemetry using LoRa-WAN to transmit sensor data to a central gateway, enabling connectivity in low-network rural environments.
- Developed a cloud-accessible Blynk dashboard for live monitoring, threshold-based alerts, and GPS-based location reporting to support rescue coordination.
- Optimized node firmware for low-power operation (sleep cycles and scheduled transmissions) and validated communication range and reliability during field tests.
- Integrated facial-recognition attendance with sensor-driven smart lighting and motion control to automate classroom management and attendance logging.
- Implemented Python-based attendance pipeline and sensor logic to trigger lighting and HVAC responses, improving energy efficiency and classroom readiness.
- Logged utilization and energy data to generate reports for instructors and administrators, enabling data-driven decisions on classroom scheduling and resource usage.
Education
Certifications
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