Embedded Engineer
Are you passionate about deploying cutting-edge machine learning models to the edge and cloud? Do you thrive in a dynamic, fast-paced environment where you can push the boundaries of what’s been done? If so, we have an exciting opportunity for an Embedded Engineer to join our team.
As an Embedded Engineer, you will play a crucial role in developing and deploying different modules/components and models on a variety of edge devices. You will be responsible for designing and implementing robust data processing pipelines that can seamlessly integrate with these edge systems and the cloud, ensuring efficient and reliable model deployment across the edge-cloud continuum.
Your expertise in technologies such as message brokers, sockets, and message queuing protocols like ZeroMQ, RabbitMQ, or Apache Kafka will be essential in building scalable and highly performant edge and cloud solutions. You will also be comfortable working with both microservices and monolithic architectures, allowing you to adapt to the unique requirements of each project.
Required Skills and Qualifications:
- Proficient in C/C++, knowledge of embedded systems programming.
- Understanding of message brokers and sockets.
- 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 sensor data acquisition, preprocessing, and integration techniques for edge devices, leveraging protocols like SPI, UART, I2C, and more.
- Experience with container technologies and container orchestration platforms for deploying and managing edge and cloud-based ML inference services.
- 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.
Would be an advantage:
- 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.
Company Benefits are discussed with Candidates specifically.
If you’re ready to revolutionize the world of embedded AI and push the boundaries of what’s possible with edge-cloud computing, we encourage you to apply for this exciting Embedded MLOps Engineer role. Join our dynamic team and be a part of shaping the future of intelligent edge and cloud systems.
Embedded