Hard Skills

- IoT Protocols: MQTT, CoAP, LoRaWAN, Zigbee

- Edge AI & Embedded ML: TensorFlow Lite, EdgeX Foundry, OpenVINO

- Cloud Platforms: AWS IoT, Azure IoT Hub, Google Cloud IoT

- Data Processing: Kafka, Apache NiFi, InfluxDB

- Programming Languages: Python, C/C++, JavaScript

- Hardware: Raspberry Pi, Arduino, NVIDIA Jetson, ESP32

- Security Standards: TLS, DTLS, Secure Element Integration

- DevOps & CI/CD: Docker, Jenkins, GitOps

Soft Skills

- Problem Solving & Critical Thinking

- Cross-Disciplinary Collaboration

- Agile & Scrum Methodologies

- Technical Documentation & Communication

- Project Management & Stakeholder Engagement

- Adaptability to Emerging Tech Trends

*Senior IoT & AI Solutions Engineer*

*Innovate IoT Solutions, San Francisco, CA*

_June 2022 – Present_

- Led the development of an AI-enabled predictive maintenance system for large industrial IoT networks, reducing downtime by 25%.

- Architected edge computing frameworks with NVIDIA Jetson devices that processed real-time sensor data locally, minimizing cloud bandwidth costs.

- Integrated deep learning models into embedded devices using TensorFlow Lite, enabling anomaly detection directly at the device level.

- Collaborated with cybersecurity teams to implement comprehensive device authentication protocols aligned with ISO 27001.

*IoT Firmware & AI Engineer*

*NextGen IoT Technologies, Palo Alto, CA*

_August 2019 – May 2022_

- Developed firmware for IoT edge devices utilizing C/C++ on ARM architectures, ensuring over-the-air update capability.

- Implemented AI inference pipelines on Raspberry Pi and ESP32 devices for environmental monitoring and smart agriculture applications.

- Designed secure data transmission channels using MQTT over TLS, increasing data integrity and privacy.

- Created dashboards and data pipelines connecting sensor streams to cloud analytics using Python and Kafka.

*IoT Software Developer*

*SmartSense Innovations, San Jose, CA*

_June 2017 – July 2019_

- Built wireless sensor node prototypes with Zigbee and LoRaWAN, enabling long-range data transmission in smart city deployments.

- Developed cloud integration modules with Node.js to aggregate device telemetry into MongoDB for real-time analytics.

- Automated deployment using Jenkins and Docker, reducing deployment time by 40%.

**Bachelor of Science in Electrical Engineering**

_Stanford University, Stanford, CA_

Graduated: 2017

- AWS Certified Solutions Architect – Associate (2023)

- Certified IoT Professional (CIoTP) – IoT Institute (2022)

- NVIDIA Deep Learning Certifications for Embedded Systems (2024)

Intelligent Manufacturing Predictive System

- Developed an edge-to-cloud architecture leveraging NVIDIA Jetson devices and AWS IoT

- Enabled real-time machine health monitoring with embedded ML models

- Resulted in 30% reduction in unforeseen equipment failures

Smart Grid Energy Optimization

- Deployed IoT sensors across residential buildings with LoRaWAN protocol

- Implemented AI algorithms to optimize energy consumption, leading to a 15% reduction in costs

- Provided stakeholders with predictive analytics dashboards for management decision-making

- **Platforms:** AWS, Azure, Google Cloud, NVIDIA Jetson, Raspberry Pi

- **Protocols:** MQTT, CoAP, Zigbee, LoRaWAN

- **Frameworks & Libraries:** TensorFlow Lite, OpenVINO, Node.js, Kafka, InfluxDB

- **Development:** Docker, Jenkins, Git, Visual Studio Code

- Python (Expert), C/C++ (Proficient), JavaScript (Intermediate)