Senior Printed Circuit Board Design Engineer

Senior Printed Circuit Board Design Engineer

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:

o Altium Designer (preferred for AI hardware complexity)

o Cadence Allegro for high-speed digital design

o 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:

o High-speed oscilloscopes (>10 GHz bandwidth)

o Logic analyzers for AI bus protocol analysis

o Thermal imaging cameras and power analyzers

o 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:

o PCI-SIG specifications (PCIe Gen4/5, CEM)

o JEDEC standards for memory interfaces

o IPC standards for PCB design and manufacturing

o 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 project