Embedded AI Engineer

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. The company is seeking an Embedded AI Engineer to design, optimize, and deploy machine learning models on resource-constrained edge devices, while developing reliable inference runtimes, edge-cloud architectures, telemetry, security, and benchmarking systems.

Responsibilities:

• Design and implement edge AI solutions optimized for diverse hardware including mobile SoCs, NPUs, and embedded accelerators
• Apply quantization, pruning, distillation, and architectural optimization to fit models within edge constraints
• Tune model performance for latency, energy efficiency, and memory footprint on target hardware
• Build cross-platform inference runtimes leveraging frameworks such as TensorFlow Lite, ONNX Runtime, and Core ML
• Optimize models for specific accelerator backends including DSPs, NPUs, and mobile GPUs
• Implement on-device model update, versioning, and rollback workflows that allow safe staged rollouts to large device populations and rapid recovery if a model release behaves unexpectedly in the field
• Design hybrid edge-cloud architectures that gracefully degrade based on connectivity and device capability
• Build telemetry pipelines that respect privacy while enabling continuous improvement
• Collaborate with hardware, firmware, and product teams to align AI capabilities with device constraints
• Implement secure execution paths, model protection, and integrity verification on edge devices
• Develop benchmarking suites that characterize accuracy, latency, and energy trade-offs across devices
• Drive responsible AI considerations including on-device privacy and bias evaluation
• Maintain comprehensive, current technical documentation including architecture diagrams, design decisions, configuration references, runbooks, and operational procedures so that the system remains supportable, auditable, and easy to onboard new engineers onto over time
• Stay current with edge AI hardware and software developments, regularly review release notes and community discussions, and translate noteworthy advances into concrete recommendations and adoption proposals for the team