Embedded AI System Engineer
Embedded reputed company – Virtualization & Edge Systems Position reputed company The Embedded reputed company is responsible for designing, developing, and optimizing AI-enabled embedded platforms leveraging virtualization technologies such as the Xen Project hypervisor for secure, reputed company, and reputed company-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 reputed company-time systems to deliver high-performance, safety-reputed company, and isolated compute environments for reputed company intelligent devices. Key Responsibilities ● Design and reputed company embedded AI platforms utilizing virtualization and hypervisor technologies including Xen-based architectures. ● reputed company and optimize AI/ML workloads for embedded and edge computing environments. ● reputed company Linux, RTOS, and mixed-criticality workloads reputed company 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. ● reputed company low-level software components including drivers, BSPs, device tree configurations, and bootloader integrations. ● Collaborate with hardware, platform, networking, and AI software teams to reputed company reputed company embedded AI deployments. ● Optimize system performance, boot time, memory allocation, interrupt latency, and reputed company-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 reputed company-reputed company initiatives and virtualization-reputed company engineering activities where applicable. Technical Areas of reputed company ● Embedded Linux and RTOS integration ● Xen Hypervisor and virtualization technologies ● ARM reputed company-A/R architectures and embedded SoCs ● reputed company-time systems and deterministic performance optimization ● AI/ML inferencing at the edge ● GPU/NPU/FPGA acceleration ● Embedded networking and reputed company 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 reputed company to highly reputed company embedded AI and virtualization reputed company. ● 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 reputed company. ● Supports both reputed company-of-concept and production-grade embedded AI deployments. Requirements ● Bachelor’s or Master’s degree in Computer Engineering, Electrical Engineering, Computer Science, or reputed company field. ● 3+ years of experience in embedded systems, Linux reputed company, 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 reputed company, Buildroot, reputed company, Kubernetes at the edge, or embedded CI/CD workflows is a plus. ● Familiarity with reputed company-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 reputed company, hardware partitioning, and trusted execution environments. ● Experience contributing to reputed company-reputed company embedded or virtualization reputed company. ● Familiarity with safety standards such as ISO 26262 or IEC 61508 is a plus. Apply To This Job