Senior AI Hardware Engineer - PCB Design Specialist About Omnidya Omnidya is a leading insurtech company revolutionizing the insurance industry through innovative technology solutions. We leverage cutting-edge AI, computer vision, and hardware/software integrations to transform insurance processes, risk assessment, and customer experiences. Role Summary We are seeking a highly skilled Senior AI Hardware Engineer with 6-10 years of specialized experience in PCB design for AI computing systems. This role focuses on designing, developing, and optimizing custom circuit boards that integrate GPUs, NPUs, and other AI accelerators for next-generation AI applications. The ideal candidate will have deep expertise in high-performance computing hardware, thermal management for AI workloads, and signal integrity optimization for AI processing systems. This position involves full-cycle AI hardware development from concept to production, working with cutting-edge AI accelerators, high-speed interfaces, and power delivery systems optimized for machine learning workloads. You will collaborate with AI software teams, system architects, and manufacturing partners to deliver robust, scalable AI hardware solutions. Key Responsibilities AI Hardware Design & Development Lead the design and development of custom PCBs for AI computing applications, including GPU and NPU integration boards Design high-performance AI accelerator cards with focus on NXP and ARM GPU integration Develop NPU integration solutions for edge AI applications using Intel Neural Compute Stick, Google Coral, and similar platforms Create multi-GPU interconnect solutions using NVLink, PCIe Gen4/5, and CXL technologies Design AI inference accelerator modules for edge computing and embedded AI applications Advanced PCB Design & Signal Integrity Execute complex PCB layouts for high-speed AI computing systems (12+ layer boards, >1000 pin BGAs) Optimize signal integrity for high-frequency AI processor interfaces (DDR4/DDR5, HBM2/HBM3) Design and validate high-speed differential pairs for AI accelerator communication buses Implement advanced routing techniques for minimizing crosstalk in dense AI computing layouts Perform pre-silicon signal integrity analysis using industry-standard simulation tools Thermal Management & Power Delivery Design sophisticated thermal solutions for high-power AI processors (300W+ TDP) Develop power delivery networks (PDN) for multi-rail AI systems with precise voltage regulation Implement advanced cooling solutions including liquid cooling interfaces for AI applications Optimize power efficiency for AI workloads through intelligent power management IC selection Design thermal monitoring and protection circuits for AI hardware safety AI Hardware Validation & Testing Develop comprehensive test strategies for AI hardware validation including ML benchmark testing Perform hardware debugging using advanced tools (high-speed oscilloscopes, logic analyzers, thermal cameras) Conduct AI workload stress testing and performance optimization Validate AI hardware against industry standards (PCIe compliance, NVIDIA GPU specifications) Execute EMI/EMC testing and mitigation for AI computing systems Cross-functional Collaboration Collaborate with AI software teams to optimize hardware-software co-design for ML workloads Work with mechanical engineers on thermal and form factor optimization for AI hardware Partner with manufacturing teams to ensure AI hardware manufacturability and cost optimization Support field deployment and troubleshooting of AI hardware systems Required Technical Skills & Experience Core AI Hardware Expertise 6-10 years of experience in embedded/AI hardware design with focus on highperformance computing Proven experience designing PCBs for GPU integration (NVIDIA GeForce, Quadro, Tesla series) Hands-on experience with NPU and AI accelerator integration (Intel VPU, Google TPU, Qualcomm NPU) Deep understanding of AI hardware architectures and computational requirements for ML workloads Advanced PCB Design Skills Expert proficiency in professional PCB design tools: Altium Designer (preferred for AI hardware complexity) Cadence Allegro for high-speed digital design Mentor Graphics PADS or KiCad for specific applications Advanced knowledge of high-speed digital design principles and methodologies Experience with signal integrity analysis tools (HyperLynx, SIwave, HFSS) Expertise in power integrity analysis and PDN optimization AI-Specific Technical Requirements Experience with high-bandwidth memory interfaces (HBM2, HBM3, GDDR6/6X) Knowledge of AI interconnect standards (NVLink, PCIe Gen4/5, CXL, InfiniBand) Understanding of AI processor power requirements and thermal design considerations Familiarity with AI software frameworks (TensorFlow, PyTorch) and their hardware implications Hardware Design & Validation Proficiency with hardware debugging and validation tools: High-speed oscilloscopes (>10 GHz bandwidth) Logic analyzers for AI bus protocol analysis Thermal imaging cameras and power analyzers JTAG debuggers and boundary scan tools Experience with DFM/DFT practices for AI hardware manufacturing Knowledge of EMI/EMC compliance for high-frequency AI systems Industry Standards & Compliance Understanding of relevant industry standards: PCI-SIG specifications (PCIe Gen4/5, CEM) JEDEC standards for memory interfaces IPC standards for PCB design and manufacturing NVIDIA GPU design guidelines and AMD GPU specifications Must-Have Qualifications Educational Background B.E./B.Tech in Electrical Engineering, Computer Engineering, or Electronics Engineering M.E./M.Tech preferred with focus on VLSI Design, Computer Architecture, or AI Hardware Experience Requirements Minimum 6 years of hands-on AI hardware or high-performance computing PCB design At least 3 projects involving GPU or NPU integration from concept to production Proven track record of AI hardware optimization for performance and power efficiency Experience with AI hardware bring-up and system-level validation Technical Certifications (Preferred) IPC Designer Certification (CID or CID+) NVIDIA hardware partner certification PCIe compliance testing experience Altium Designer certification Preferred Qualifications Advanced AI Hardware Experience Experience with FPGA-based AI acceleration (Xilinx Versal, Intel Stratix) Knowledge of neuromorphic computing architectures and implementation Familiarity with quantum computing hardware interfaces Experience with AI chip design or custom ASIC integration Specialized Applications Automotive AI hardware design with AEC-Q qualification Data center AI infrastructure design and optimization Edge AI systems with power and thermal constraints AI camera systems with real-time processing requirements Advanced Tools & Technologies Experience with AI hardware simulation tools (Ansys, Synopsys) Knowledge of advanced cooling solutions (vapor chambers, liquid cooling) Familiarity with AI benchmarking and performance analysis tools Experience with hardware security for AI systems Industry Knowledge Understanding of AI workload characteristics and hardware optimization Knowledge of AI hardware trends and emerging technologies Familiarity with AI hardware startups and ecosystem dynamics Experience with AI hardware patents and intellectual property What We Offer Opportunity to work on cutting-edge AI hardware that shapes the future of computing Collaborative environment with leading AI researchers and engineers Access to latest AI hardware development tools and technologies Career growth path in the rapidly expanding AI hardware field Competitive compensation Professional development through conferences and training programs Eligibility Requirements Legal authorization to work in the intended country Ability to handle confidential and proprietary AI hardware designs Willingness to travel for hardware validation and customer support (up to 20%) Strong communication skills for cross-functional collaboration in AI projects
Job Title: DevOps Engineer Location: Ahmedabad / Remote (Hybrid flexibility) Department: Engineering & Infrastructure Reports To: CTO / Head of Software Development ________________________________________ About Omnidya Tech LLP, Hello Omnidya is building Indias first advanced AI-powered dashcam ecosystem for fleet management, safety analytics, and smart transportation. Our platform fuses edge AI processing (ADAS, DMS, ANPR, telematics) with secure cloud connectivity (AWS IoT, S3, MQTT, and real-time streaming). We are seeking a DevOps Engineer to scale our infrastructure, automate build and deployment pipelines, and manage GPU-based AI compute clusters both on-premise and in the cloud. ________________________________________ Role Overview As a DevOps Engineer, you will play a crucial role in automating deployments, managing distributed edge-cloud systems, and maintaining our GPU training and inference environments. You’ll work closely with the AI, firmware, and backend teams to ensure smooth CI/CD workflows, optimal GPU utilization, and high system reliability. ________________________________________ Key Responsibilities CI/CD & Automation Design, build, and maintain CI/CD pipelines using GitLab CI, Jenkins, or GitHub Actions for backend, AI, and firmware builds. Automate testing and deployment for Yocto-based embedded systems (i.MX8 platforms). Create Docker containers and deployment scripts for AI inference and cloud microservices. Cloud & Infrastructure Management Manage and scale AWS infrastructure (IoT Core, EC2, ECR, CloudWatch, Lambda, Route 53). Set up and maintain Terraform or CloudFormation for Infrastructure as Code (IaC). Implement robust monitoring, alerting, and log aggregation using Prometheus, Grafana, ELK, or CloudWatch. GPU Rack & Compute Cluster Management Manage on-premise GPU servers / AI training racks (Ubuntu-based, multi-GPU systems). Configure, optimize, and monitor GPU utilization for PyTorch / TensorFlow workloads. Handle CUDA driver updates, containerized training environments, and model deployment pipelines. Automate job scheduling using Slurm, Docker Swarm, or Kubernetes for GPU workloads. Monitor performance metrics (GPU load, memory, thermals, power usage) to ensure stable training and inference operations. Device Integration & Fleet Management Streamline OTA (Over-The-Air) update pipelines for connected edge devices. Manage provisioning, authentication, and status monitoring of thousands of IoT devices. Ensure robust MQTT, REST API, and video data sync between dashcams and the cloud. Security & Compliance Implement AWS IAM policies, TLS/SSL certificates, and secure OTA mechanisms. Collaborate on device and cloud-level security hardening for regulatory compliance (BIS, ICAT). Documentation & Collaboration Document automation flows, deployment topologies, and infrastructure standards. Collaborate with AI, embedded, and backend teams to align deployment processes across systems. ________________________________________ Required Skills & Experience Experience 3–7 years of experience in DevOps, Cloud Infrastructure, or Site Reliability Engineering. Technical Skills Linux system administration (Ubuntu, Yocto, Debian) Containerization: Docker, Podman, Kubernetes (preferably K3s / MicroK8s) CI/CD Tools: GitLab CI, Jenkins, GitHub Actions Cloud Platforms: AWS (EC2, IoT Core, S3, Lambda, CloudWatch) IaC: Terraform, CloudFormation Monitoring: Prometheus, Grafana, ELK Stack Networking: VPN, DNS, load balancing, NAT, SSL certificates GPU Systems: Hands-on with NVIDIA GPU drivers, CUDA, cuDNN, TensorRT Experience with GPU workload management, thermal/power profiling, and optimization Familiarity with multi-GPU training, inference scaling, and model deployment Bonus Skills Experience with embedded Linux (Yocto, NXP i.MX8) Understanding of RTMP/FLV streaming pipelines or GStreamer Familiarity with Python microservices (FastAPI / Flask) Knowledge of AI/ML model lifecycle management (training quantization edge inference) ________________________________________ Soft Skills Strong analytical and problem-solving mindset. Excellent communication and cross-functional collaboration. Passion for automation, reliability, and scalability. Ability to work independently in a fast-paced startup environment. ________________________________________ What We Offer Competitive salary and performance-based bonuses. Opportunity to work on cutting-edge edge-AI + GPU infrastructure projects. Exposure to AWS, IoT, AI training clusters, and fleet-scale deployment systems. Hybrid work setup and rapid growth opportunities in a high-impact product team.