Embedded AI System Engineer

Embedded AI Engineer – Virtualization & Edge Systems
Position Summary
The Embedded AI Engineer is responsible for designing, developing, and optimizing AI-enabled embedded platforms leveraging virtualization technologies such as the Xen Project hypervisor for secure, scalable, and real-time edge computing environments. This role focuses on integrating AI workloads with embedded operating systems, RTOS platforms, and virtualized infrastructure for automotive, robotics, industrial, telecom, and edge AI applications.
The engineer will work across embedded Linux, hypervisors, hardware acceleration, AI frameworks, and real-time systems to deliver high-performance, safety-focused, and isolated compute environments for next-generation intelligent devices.

Key Responsibilities
● Design and develop embedded AI platforms utilizing virtualization and hypervisor technologies including Xen-based architectures.
● Develop and optimize AI/ML workloads for embedded and edge computing environments.
● Integrate Linux, RTOS, and mixed-criticality workloads within virtualized embedded systems.
● Configure and optimize Xen Hypervisor environments for ARM, x86, and embedded SoC platforms.
● Support AI acceleration technologies including GPUs, NPUs, FPGAs, and hardware-assisted virtualization.
● Implement secure workload isolation, resource partitioning, and fault-tolerant embedded architectures.
● Develop low-level software components including drivers, BSPs, device tree configurations, and bootloader integrations.
● Collaborate with hardware, platform, networking, and AI software teams to enable scalable embedded AI deployments.
● Optimize system performance, boot time, memory allocation, interrupt latency, and real-time responsiveness.
● Support containerization, VM orchestration, and edge deployment automation for embedded systems.
● Participate in debugging, profiling, benchmarking, and performance tuning activities across embedded platforms.
● Contribute to open-source initiatives and virtualization-related engineering activities where applicable.
 

Technical Areas of Focus
● Embedded Linux and RTOS integration
● Xen Hypervisor and virtualization technologies
● ARM Cortex-A/R architectures and embedded SoCs
● Real-time systems and deterministic performance optimization
● AI/ML inferencing at the edge
● GPU/NPU/FPGA acceleration
● Embedded networking and security isolation
● Functional safety and secure compute architectures
● Edge AI orchestration and containerized workloads
● Automotive, industrial, robotics, and telecom embedded platforms
 

Scope & Complexity
● Works on moderately complex to highly complex embedded AI and virtualization projects.
● Designs solutions supporting mixed-criticality and multi-OS embedded environments.
● Requires strong collaboration across embedded software, hardware, infrastructure, and AI engineering teams.
● Participates in architecture reviews, performance optimization, and platform design decisions.
● Supports both proof-of-concept and production-grade embedded AI deployments.
 

Requirements
● Bachelor’s or Master’s degree in Computer Engineering, Electrical Engineering, Computer Science, or related field.
● 3+ years of experience in embedded systems, Linux platform engineering, or virtualization technologies.
● Strong experience with C/C++, Python, and embedded software development.
● Familiarity with Xen Hypervisor, KVM, QEMU, or similar virtualization technologies preferred.
● Experience with ARM-based embedded platforms and Linux kernel concepts.
● Knowledge of AI/ML frameworks such as TensorFlow, PyTorch, ONNX, or TensorRT.
● Understanding of embedded networking, memory management, interrupt handling, and low-level system architecture.
● Experience with Yocto, Buildroot, Docker, Kubernetes at the edge, or embedded CI/CD workflows is a plus.
● Familiarity with real-time systems, functional safety, or automotive embedded environments preferred.
● Strong debugging, problem-solving, and performance optimization skills.
 

Preferred Qualifications
● Experience with embedded AI inferencing optimization and edge deployment architectures.
● Exposure to automotive hypervisors, industrial automation, robotics, or telecom edge systems.
● Understanding of virtualization security, hardware partitioning, and trusted execution environments.
● Experience contributing to open-source embedded or virtualization projects.
● Familiarity with safety standards such as ISO 26262 or IEC 61508 is a plus.