Edge AI Engineer – Remote. Location: Secaucus, New Jersey, United States. Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a full-time, direct-hire position. We are seeking an Edge AI Engineer to design, optimize, and deploy machine learning models that run efficiently on resource-constrained edge devices, including mobile platforms, embedded systems, and specialized accelerators. The role requires deep expertise in model compression, quantization, and hardware-aware optimization, along with strong systems engineering skills to ship reliable AI capabilities outside the data center. The ideal candidate has shipped edge AI in production environments where compute, memory, energy, and connectivity constraints shape engineering trade-offs. Key responsibilities include designing and implementing edge AI solutions for mobile SoCs, NPUs, and embedded accelerators; applying quantization, pruning, distillation, and architectural optimization; tuning performance for latency, energy, and memory footprint; building cross-platform inference runtimes leveraging frameworks such as TensorFlow Lite, ONNX Runtime, and Core ML; optimizing models for specific accelerator backends including DSPs, NPUs, and mobile GPUs; implementing on-device model updates, versioning, and rollback workflows for safe staged rollouts and rapid recovery if a model release behaves unexpectedly; designing hybrid edge-cloud architectures that gracefully degrade based on connectivity and device

Edge AI Engineer – Remote. Location: Secaucus, New Jersey, United States. Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a full-time, direct-hire position. We are seeking an Edge AI Engineer to design, optimize, and deploy machine learning models that run efficiently on resource-constrained edge devices, including mobile platforms, embedded systems, and specialized accelerators. The role requires deep expertise in model compression, quantization, and hardware-aware optimization, along with strong systems engineering skills to ship reliable AI capabilities outside the data center. The ideal candidate has shipped edge AI in production environments where compute, memory, energy, and connectivity constraints shape engineering trade-offs. Key responsibilities include designing and implementing edge AI solutions for mobile SoCs, NPUs, and embedded accelerators; applying quantization, pruning, distillation, and architectural optimization; tuning performance for latency, energy, and memory footprint; building cross-platform inference runtimes leveraging frameworks such as TensorFlow Lite, ONNX Runtime, and Core ML; optimizing models for specific accelerator backends including DSPs, NPUs, and mobile GPUs; implementing on-device model updates, versioning, and rollback workflows for safe staged rollouts and rapid recovery if a model release behaves unexpectedly; designing hybrid edge-cloud architectures that gracefully degrade based on connectivity and device