Key Responsibilities:

• High-Level Design and Architecture
• Influence the Edge AI strategy by providing expert advice on design and architecture.
• Make critical decisions regarding technical directions, scalability, and system performance.
• Develop and optimize AI inference models for deployment on edge devices with embedded GPU/TPU accelerators, focusing on local Low Latency Model (LLM) inference.
• Implement and fine-tune low-latency model inference pipelines to meet real-time performance requirements.
• Collaborate with cross-functional teams to integrate AI inference solutions into edge computing platforms and applications.
• Collaborate with the GPU Hardware Design Team to design and optimize GPUs that power next-generation devices.
• Conduct performance profiling and optimization to maximize the efficiency of GPU/TPU acceleration for local LLM inference.
• Work on micro-architecture development, ensuring efficient execution of graphics, compute, and AI workloads within energy and area constraints.
• Stay current with advancements in GPU/TPU technologies and edge AI frameworks, incorporating them into solution designs as appropriate.
• Provide technical expertise and support to project teams, ensuring successful implementation and deployment of edge AI solutions.

Team Leadership:

• Lead and inspire a team of engineers, providing guidance, setting goals, and ensuring collaboration.
• Oversee project planning, execution, and delivery, ensuring alignment with business objectives.
• Manage all phases of technical projects, from conception to completion.
• Develop project specifications, track progress, and control costs.
• Foster a positive work environment, encouraging professional growth and knowledge sharing.

Qualifications:

• Bachelor’s degree in computer science, Engineering, or a related field; Master’s degree preferred.
• 5+ years of hands-on experience in AI model development and deployment, with a focus on edge computing and local LLM inference.
• Strong programming skills in languages such as Python and C++
• Proficiency in LLM frameworks (e.g., vLLM, Text generation inference, OpenLLM, Ray Serve, and HuggingFace Transformers) and deep learning libraries.
• Extensive experience with GPU/TPU acceleration for AI inference, including optimization techniques (tensor, pipeline, data, sharded data parallelism) and performance tuning,
• Hands on experience with one or more GPU frameworks: CUDA, Vulkan, OpenCL
• Deep knowledge of GPU memory layout, familiarity with NVIDIA Jatison, ARM Mali or relevant SoC configurations.
• Knowledge of parallel computation, memory scheduling, and structural optimization
• Excellent problem-solving and analytical skills, with a passion for innovation and continuous learning.

Additional Skills (Preferred):

• Experience with edge device hardware and software integration.
• Familiarity with edge computing architectures and IoT platforms.
• Experience with edge AI applications in domains such as robotics, autonomous vehicles, or industrial automation.

• If you are a skilled Edge AI Engineer with a passion for pushing the boundaries of edge computing and GPU/TPU acceleration, particularly in local LLM inference, we want to hear from you! Join us in shaping the future of AI at the edge and revolutionizing industries with innovative edge AI solutions. Apply now to be part of our dynamic and collaborative team!