Edge Platform Lead

JD Edge Platform Lead

Location: Kandivali, Mumbai (Preferred)
Experience: 9- 15+ Years
Department: Engineering / Platform Engineering

Role Overview:

We are seeking an experienced Edge Platform Lead to drive the architecture, qualification, optimization, and lifecycle management of edge computing platforms used in AI-powered industrial inspection, machine vision, and automation solutions. The role requires expertise across hardware platforms, GPU acceleration, AI inference, embedded systems, operating systems, and edge-to-cloud architectures.

Key Responsibilities

• Define, evaluate, and standardize edge hardware platforms including Industrial PCs, Embedded Devices, GPUs, and AI Accelerators.
• Lead hardware qualification, benchmarking, thermal testing, reliability validation, and performance optimization.
• Optimize AI inference workloads using technologies such as CUDA, TensorRT, cuDNN, and NVIDIA GPU platforms.
• Manage and tune Windows and Linux environments for high-performance industrial deployments.
• Design scalable edge-to-cloud and client-server architectures with strong networking fundamentals.
• Evaluate and optimize machine vision systems including industrial cameras, image acquisition pipelines, frame grabbers, and GPU-based image processing.
• Establish platform validation methodologies, benchmark suites, and qualification standards.
• Collaborate with AI, Image Processing, Software Engineering, QA, Product Management, and hardware vendors to drive platform strategy and technical decisions.

Required Skills

• Edge Computing & Platform Engineering
• Embedded Systems & Industrial PCs
• NVIDIA GPUs, CUDA, TensorRT, cuDNN
• AI Inference Optimization & Deep Learning Deployment
• Linux & Windows System Performance Tuning
• Machine Vision / Computer Vision Systems
• Industrial Cameras (GigE Vision, USB3 Vision, CoaXPress)
• Networking, TCP/IP, Client-Server Architecture
• Hardware Qualification, Benchmarking, Thermal & Reliability Testing

Preferred Skills

• NVIDIA Jetson, DeepStream
• Docker, Kubernetes
• ONNX Runtime, OpenVINO
• Embedded Linux, RTOS
• Industrial Automation, PLC Communication
• Edge AI Deployments and High-Speed Vision Systems

Ideal Candidate Profile

• 915+ years of experience in Embedded Systems, Edge Computing, Industrial Automation, Machine Vision, or AI Platforms.
• Minimum 5 years of hands-on experience with GPU-accelerated platforms.
• Strong background in hardware qualification, AI inference optimization, and industrial vision solutions.
• Experience supporting production deployments across Linux and Windows environments.