Machine Learning Engineer In Iot Resume Example
Professional ATS-optimized resume template for Machine Learning Engineer In Iot positions
John Doe
Senior Machine Learning Engineer | IoT Specialist
Email: johndoe@email.com | Phone: (123) 456-7890 | LinkedIn: linkedin.com/in/johndoe | GitHub: github.com/johndoe
PROFESSIONAL SUMMARY
Innovative and results-driven Machine Learning Engineer with over 7 years of experience designing and deploying intelligent IoT solutions. Expertise in building scalable ML models for smart devices, integrating real-time data streams, and optimizing predictive analytics for industrial and consumer IoT applications. Proven ability to lead cross-functional teams, develop edge-optimized ML algorithms, and leverage cloud-native architectures to deliver robust, low-latency IoT systems aligned with Industry 4.0 standards.
SKILLS
**Hard Skills:**
- Deep Learning (TensorFlow, PyTorch)
- Edge ML Deployment (TensorFlow Lite, MLKit)
- IoT Protocols (MQTT, CoAP, OPC UA)
- Cloud Platforms (AWS IoT, Azure IoT Hub, Google Cloud IoT)
- Data Engineering & Streaming (Apache Kafka, Apache Spark)
- Sensor Data Processing & Analytics
- Model Optimization & Quantization for Edge Devices
- Python, C++, Java, SQL
**Soft Skills:**
- Problem-solving & Critical Thinking
- Cross-functional Collaboration
- Agile Development & Scrum
- Technical Documentation & Knowledge Sharing
- Adaptability in Emerging Tech Environments
WORK EXPERIENCE
*Senior Machine Learning Engineer | TechSense IoT Solutions*
San Francisco, CA | Jan 2023 – Present
- Led the development of an edge AI framework for a fleet of smart industrial sensors, reducing data transmission costs by 40% through onboard model inference.
- Architected a real-time anomaly detection system utilizing sensor fusion data streams, resulting in a 25% decrease in machine downtime.
- Collaborated with hardware teams to optimize ML models for low-power embedded devices, employing quantization and pruning techniques.
- Developed cloud-native pipelines on AWS IoT and Lambda functions for centralized model updates and alerts.
*IoT & ML Engineer | Innovatech Smart Devices*
Austin, TX | Aug 2019 – Dec 2022
- Designed predictive maintenance models for consumer home automation products, increasing customer satisfaction and reducing warranty claims by 15%.
- Implemented multi-modal sensor data handling combined with deep learning models, improving device activity classification accuracy by 12%.
- Managed end-to-end deployment of ML models on edge gateways, utilizing Docker containers and CI/CD workflows.
- Integrated MQTT-based messaging protocols with cloud analytics dashboards for real-time device monitoring.
Data Scientist | GreenGrid Energy Solutions
Remote | Jan 2017 – Jul 2019
- Developed energy consumption forecasting models using time-series analysis and LSTM networks to optimize grid load balancing.
- Built an IoT data lake on Azure Data Lake with automated ETL pipelines, yielding faster insights into power usage patterns.
- Conducted sensor data quality assessments, reducing data noise by 20% through preprocessing pipelines.
EDUCATION
**Master of Science in Electrical Engineering**
University of California, Berkeley | 2014 – 2016
**Bachelor of Science in Computer Engineering**
Texas A&M University | 2010 – 2014
CERTIFICATIONS
- Certified IoT Professional (CIoTP) – IoT Association, 2024
- TensorFlow Developer Certificate – Google, 2023
- AWS Certified Solutions Architect – Associate, 2022
PROJECTS
Smart Agriculture IoT Platform
- Developed a machine learning-driven soil moisture and crop health monitoring system utilizing custom sensor arrays and edge devices.
- Deployed lightweight CNN models on Raspberry Pi-based gateways, enabling real-time insights to farmers via mobile app notifications.
Predictive Asset Monitoring System for Manufacturing
- Created a scalable pipeline combining sensor data ingestion, anomaly detection, and predictive analytics using Kafka and Spark.
- Achieved over 85% accuracy in failure prediction, proactively guiding maintenance teams, saving an estimated $2M annually.
TOOLS & TECHNOLOGIES
- ML Frameworks: TensorFlow, PyTorch, Scikit-learn
- Cloud Services: AWS IoT, Azure IoT, Google Cloud IoT, Lambda, Functions
- Protocols & Edge Tech: MQTT, CoAP, OPC UA, TensorFlow Lite, Edge TPU
- Data Storage: DynamoDB, InfluxDB, Hadoop
- Development: Python, C++, Java, Docker, Kubernetes
- Monitoring & Visualization: Grafana, Kibana
LANGUAGES
- English (Native)
- Spanish (Professional Working Proficiency)
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