Embedded Edge AI Architect

We are seeking an experienced Embedded Edge AI Architect with strong expertise in AI/ML modeling, embedded systems, and telemetry-driven performance optimization. The ideal candidate will have hands-on experience developing and deploying AI solutions on embedded platforms such as ARM processors, GPUs, DSPs, FPGAs, and MCUs, with a strong background in Python, C/C++, and Edge AI frameworks.

• 8-10+ years of experience in Embedded Systems, Edge AI, AI/ML, Data Engineering, Telemetry, or Applied Data Science.
• Strong proficiency in Python for data processing, AI/ML modeling, and pipeline development.
• Hands-on experience with C/C++ for embedded software and firmware integration.
• Experience developing and deploying Edge AI applications on embedded devices.
• Strong knowledge of AI/ML frameworks such as TensorFlow, PyTorch, TensorFlow Lite, Core ML, TensorRT, or ONNX Runtime.
• Experience working with embedded platforms including ARM processors, NVIDIA GPUs, DSPs, FPGAs, or Microcontrollers (MCUs).
• Proven experience designing and building telemetry pipelines, data ingestion frameworks, and data models.
• Experience translating hardware and firmware telemetry into structured datasets for analytics, performance tuning, and machine learning.
• Strong understanding of time-series data, signal processing, and real-time data systems.
• Experience developing firmware-to-application interfaces and telemetry ingestion pipelines.
• Ability to work across multiple system layers, including embedded devices, transport, host systems, and data platforms.
• Familiarity with GPU-accelerated computing and high-performance computing environments.
• Experience collaborating with firmware, hardware, and platform engineering teams.
• Experience designing performance optimization models based on telemetry, workload behavior, thermal constraints, and system utilization.

Preferred Qualifications

• Experience deploying AI inference pipelines on NVIDIA Jetson, CUDA, or TensorRT.
• Experience building Edge AI applications using Core ML, Metal, TensorFlow Lite, or OpenCL/OpenGL ES.
• Knowledge of Embedded Linux environments.
• Familiarity with RTOS concepts such as Zephyr or FreeRTOS.
• Experience with platform management protocols such as MCTP and PLDM.
• Exposure to hybrid Edge/Cloud architectures.
• Experience with data observability, telemetry validation, and data integrity frameworks.
• Background in hardware-centric environments such as IoT, Robotics, Autonomous Systems, Consumer Devices, or Industrial Automation.

Key Technologies

• Programming: Python, C/C++
• AI/ML: TensorFlow, PyTorch, TensorFlow Lite, Core ML, TensorRT, ONNX Runtime
• Embedded Platforms: ARM, NVIDIA GPU, DSP, FPGA, MCU
• Operating Systems: Embedded Linux, RTOS (Zephyr, FreeRTOS)
• GPU Technologies: CUDA, TensorRT
• Telemetry & Analytics: Time-Series Data, Signal Processing, Data Pipelines
• Protocols: MCTP, PLDM