Embedded Engineer (вакансія неактивна)

Required Skills and Qualifications:

• Proficient in Python, C/C++, and professional knowledge of embedded systems programming.
• Extensive experience in developing and deploying machine learning models on edge devices.
• Deep understanding of message brokers, sockets, and technologies like ZeroMQ, RabbitMQ, or Apache Kafka for building scalable and efficient edge and cloud data processing pipelines.
• Expertise in designing and implementing robust data processing pipelines that can seamlessly integrate with edge devices and cloud infrastructure, handling various data types such as images, videos, text, and audio.
• Familiarity with microservices and monolithic architectures, and their tradeoffs in the context of edge-cloud communication and data flow.
• Familiarity with sensor data acquisition, preprocessing, and integration techniques for edge devices, leveraging protocols like SPI, UART, I2C, and more.
• Knowledge of CI/CD tools and practices, such as Jenkins, Travis CI, or GitHub Actions, to automate the deployment of ML models across the edge-cloud continuum.
• Proficiency in embedded systems programming, including low-level hardware interaction, device drivers, and firmware development for seamless data exchange between edge devices and the cloud.
• Strong problem-solving and analytical skills, with the ability to think critically and find creative solutions for edge-cloud ML deployments.
• Excellent verbal and written communication skills, with the ability to
  effectively collaborate with cross-functional teams.

Preferred Experience:

• Experience working with UAVs, drones, or flight controllers, and their integration with embedded AI systems for real-time inference and data processing
• Knowledge of digital video (HW, protocols, processing, encryption).
• Familiarity with edge-cloud synchronization protocols and mechanisms, such as MQTT, CoAP, or AMQP, for efficient and reliable data transfer between the edge and the cloud.
• Knowledge of robotic frameworks (e.g., ROS, ROS2, Ardupilot) and their application in edge-cloud computing environments for robotics and autonomous systems.
• Experience with time-series data analysis and anomaly detection on edge devices, and integrating these insights with cloud-based data analytics and visualization platforms.