Solution Architect : Edge AI

We are seeking a Solution Architect for Edge AI with 10+ years of experience to lead the architecture, design, and technology strategy for next-generation embedded and intelligent edge products.

The ideal candidate will possess extensive experience in solution architecture, system design, embedded systems engineering, and product development, with the ability to translate business requirements into scalable and robust technical solutions. This role requires strong expertise in hardware-software systems, platform selection, performance optimization, resource planning, and cross-functional technical leadership.

Candidates with experience in domains such as ADAS/Automotive Electronics, Robotics, Industrial Automation, Autonomous Systems, or other safety-critical embedded systems are preferred.

Key Responsibilities

• Define and drive end-to-end system and solution architectures for embedded and intelligent edge products.

• Translate business and product requirements into scalable, high-performance technical solutions.

• Lead hardware platform evaluation, selection, benchmarking, and architecture trade-off analysis.

• Drive system-level design decisions including compute, memory, storage, communication, and resource budgeting.

• Provide technical leadership for the development of PoCs, prototypes, and MVPs, ensuring alignment with product and business objectives.

• Collaborate with cross-functional teams across hardware, software, validation, product management, and customer engineering to ensure successful product delivery.

• Mentor engineering teams and establish architecture best practices, design standards, and technical governance.

• Evaluate emerging technologies and recommend innovative solutions to enhance product capabilities and competitiveness.

Experience

• 10+ years of experience in Solution Design, System Architecture, or Embedded Product Development.

• Proven experience defining and delivering end-to-end architectures for complex embedded systems.

• Strong experience in hardware-software system integration and embedded product lifecycle management.

• Experience working in one or more of the following domains:

• ADAS / Automotive Electronics

• Industrial Automation

• Autonomous Systems

• Safety-Critical Embedded Systems

Technical Expertise

Embedded Systems

• Embedded C/C++
• Embedded Linux
• Real-Time Operating Systems (RTOS) such as QNX, VxWorks, FreeRTOS, Zephyr, or equivalent
• Experience with ROS (Robot Operating System)
• Multi-core Processor Architectures
• BSP, Middleware, and Device Interface Understanding
• Real-Time and Distributed Systems

Hardware Platforms

• ARM-based Processors and SoCs
• NXP, TI, Renesas, Qualcomm, NVIDIA, STMicroelectronics, or similar platforms
• Processor, Memory, and Storage Architecture
• Hardware Benchmarking and Platform Evaluation
• Edge Computing Platforms

Development Tools

• Git, GitLab, GitHub
• Docker
• CI/CD Pipelines
• JIRA, Confluence
• Debugging, Profiling, and Performance Analysis Tools

AI/ML & Edge AI

• Experience architecting AI-enabled embedded or intelligent edge solutions.

• Familiarity with deep learning frameworks such as:

• PyTorch

• TensorFlow

• TensorFlow Lite (TFLite)

• ONNX Runtime

• Exposure to edge AI deployment and optimization technologies:

• NVIDIA TensorRT

• NVIDIA DeepStream

• NXP eIQ

• Intel OpenVINO

• Qualcomm AI Engine SDK

• Understanding of:

• Model optimization and quantization

• Inference deployment on edge devices

• AI accelerator architectures

• Performance benchmarking of AI workloads

Vision & Autonomous Systems

• ROS / ROS2
• Computer Vision and Image Processing Systems
• LiDAR, Radar, Camera, and IMU-based Systems
• Autonomous and Intelligent Edge Platforms