Mlops Engineer In Iot Resume Example
Professional ATS-optimized resume template for Mlops Engineer In Iot positions
JOHN DOE
MLOps Engineer | IoT
Email: johndoe@email.com | Phone: (123) 456-7890 | LinkedIn: linkedin.com/in/johndoe | GitHub: github.com/johndoe
PROFESSIONAL SUMMARY
Results-driven MLOps Engineer specialized in deploying, scaling, and maintaining machine learning models within IoT ecosystems. Adept at implementing robust CI/CD pipelines, managing real-time data streams, and optimizing edge-device integrations. Skilled at bridging the gap between data science and DevOps, delivering scalable solutions that enhance IoT device intelligence and operational efficiency. Passionate about leveraging emerging technologies such as federated learning and edge AI to drive innovation in connected environments.
SKILLS
**Hard Skills**
- IoT Data Pipeline Development (Apache Kafka, MQTT)
- Cloud Platforms (AWS IoT, Azure IoT Hub, Google Cloud IoT)
- Containerization & Orchestration (Docker, Kubernetes)
- ML Model Deployment (TensorFlow Serving, TorchServe)
- Automated ML Pipelines (MLflow, Kubeflow)
- Edge Computing & Firmware Integration
- Data Streaming & Real-Time Analytics
- CI/CD Automation (Jenkins, GitLab CI/CD)
- Monitoring & Logging (Prometheus, Grafana, ELK Stack)
- Security & Compliance in IoT deployments
**Soft Skills**
- Cross-team Collaboration
- Problem Solving & Critical Thinking
- Agile Methodologies
- Communication & Technical Documentation
- Continuous Learning & Innovation
WORK EXPERIENCE
*Senior IoT MLOps Engineer*
*Innovate IoT Solutions, San Francisco, CA*
June 2022 – Present
- Designed and implemented end-to-end ML deployment pipelines for IoT sensor data, reducing model rollout times by 40%.
- Led migration of on-device inference models to edge devices using TensorFlow Lite and Edge TPU, improving latency by 35%.
- Developed an automated monitoring system with Prometheus & Grafana, enabling proactive anomaly detection in real-time sensor data.
- Collaborated with data scientists to implement federated learning strategies, improving privacy compliance and model accuracy across distributed devices.
*IoT & MLOps Engineer*
*SmartConnect Technologies, Seattle, WA*
March 2019 – May 2022
- Managed deployment and scaling of ML models for connected industrial IoT applications, supporting over 50,000 devices globally.
- Built a secure, scalable MQTT-based messaging pipeline integrated with Kafka for real-time data ingestion and processing.
- Implemented CI/CD pipelines using Jenkins and Docker, reducing deployment failures and streamlining releases.
- Developed edge firmware with embedded Python and integrated it with cloud services to enable real-time local inference.
*IoT Data Engineer*
*NextGen IoT Labs, Boston, MA*
January 2017 – February 2019
- Developed data pipelines for aggregating and preprocessing IoT device telemetry.
- Collaborated on the deployment of embedded AI models on microcontrollers using PlatformIO and OpenMV for predictive maintenance.
- Conducted security audits for IoT data transport and storage, ensuring compliance with industry standards such as ISO/IEC 27001.
EDUCATION
**Bachelor of Science in Computer Science**
Massachusetts Institute of Technology (MIT)
Graduated: 2016
CERTIFICATIONS
- Certified Kubernetes Administrator (CKA)
- AWS Certified Machine Learning – Specialty
- Edge AI & Computer Vision Certification – NVIDIA Deep Learning Institute
PROJECTS
Federated Learning for Smart City Sensors
Developed a federated learning framework to train models across distributed smart city sensors, maintaining data privacy while improving local anomaly detection accuracy by 25%.
Real-time IoT Analytics Dashboard
Built a scalable analytics platform utilizing Kafka, Spark Streaming, and Grafana to visualize real-time sensor health metrics across industrial sites, reducing downtime by 15%.
Edge Device Optimization Suite
Created an optimization pipeline for deploying lightweight ML models on constrained edge devices, improving inference speed and reducing power consumption by 30%.
TOOLS & TECHNOLOGIES
- Cloud: AWS IoT, Azure IoT, Google Cloud IoT
- DevOps: Docker, Kubernetes, Jenkins, GitLab CI/CD
- Data Streaming: Kafka, MQTT, RabbitMQ
- ML Frameworks: TensorFlow, PyTorch, ONNX
- Monitoring: Prometheus, Grafana, ELK Stack
- IoT Protocols & Standards: MQTT, CoAP, LwM2M
LANGUAGES
- Python (Expert)
- C/C++ (Intermediate)
- SQL (Proficient)
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