Embedded Edge AI Engineer

Creative Synergies Group; Bengaluru; On-site; INR 600,000 - 900,000; Full time

Job summary

A technology solutions company located in Bengaluru is seeking a candidate with robust experience in AI and embedded systems. The ideal applicant should demonstrate expertise in Embedded C or C++, Python, and be knowledgeable about deploying AI models on embedded devices. Familiarity with various protocols and containerization platforms like Dockers and Kubernetes is highly desirable. Candidates should also have practical experience with camera subsystems and FPGA-based image processing.

Qualifications

• Experience working with AI capable MCU’s or GPU’s.
• Knowledge on AI/ML edge inference for vision systems.
• Strong understanding on various hardware peripheral protocols (Ethernet, I2C, SPI, UART, MIPI-CSI2).
• Experience working with various Edge containerization platforms.
• Familiarity with media frameworks like GStreamer and OpenCV; edge containerization (Docker/Kubernetes).

Skills

Embedded C or C++

Python/scripting

AI/ML edge inference

FPGA based image processing

GStreamer

OpenCV

Dockers

Kubernetes

Embedded C/C++

Python scripting

Firmware development

Docker/Kubernetes

Tools

Docker

Kubernetes

Job description

• Experience working with AI capable MCU’s or GPU’s
• Knowledge on AI/ML edge inference for vision systems
• Deploying ML/DL models on embedded device and guiding team on optimizing AI workloads.
• Hands-on experience working with camera subsystems and with FPGA based image processing
• Strong understanding on various hardware peripheral protocols (Ethernet, I2C, SPI, Ethernet, UART, MIPI-CSI2)
• Deploying ML/DL models on embedded device and guiding team on optimizing AI workloads.
• Hands-on experience working with camera subsystems and with FPGA based image processing
• Experience working with media frameworks like GStreamer and familiarity with OpenCV
• Strong expertise in Embedded C or C++, Python/scripting and low-level firmware working with various processors.
• Experience working with various Edge containerization platforms (Dockers, Kubernetes, etc.)