Svarock

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.

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 Python, C/C++, and professional knowledge of embedded
  systems programming.
• Extensive experience in developing and deploying machine learning modelson 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.
• Experience with container technologies (e.g., Docker, Podman) and
  container orchestration platforms (e.g., Kubernetes, OpenShift) for
  deploying and managing edge and cloud-based ML inference services. 
• 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.
• Understanding of edge computing challenges, including resource
  constraints, power management, latency, and offline operation.
  Experience with embedded operating systems, such as Linux (e.g.,
  Raspbian, Ubuntu Server) and real-time OSes (e.g., FreeRTOS, NuttX), and their integration with edge ML inference services.
• 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.