AI Engineer

• Design, build, and maintain real-time 3D spatial processing pipelines using sensor fusion from LiDAR, camera feeds, and spatial telemetry
• Filter and segment point clouds and perform 3D layout analysis
• Develop computer vision models and post-processing algorithms to extract structural features and recognize building geometry
• Generate precise 2D/3D floorplans from raw visual and spatial data
• Translate research findings, algorithmic prototypes, and AI/ML/LLM concepts into high-performance, maintainable Python production code
• Own core product implementations
• Architect, deploy, and manage multi-model inference pipelines on AWS SageMaker and NVIDIA Triton Inference Server
• Build end-to-end data and MLOps pipelines including synthetic data generation, active annotation, dataset versioning, CI/CD, and real-time telemetry
• Evaluate, deploy, and monitor model performance, latency, and spatial accuracy
• Collaborate with software engineering, research, and product management teams to transition prototypes into scalable, market-ready releases

• Production-level mastery of Python alongside working knowledge of C++ or Swift
• Hands-on experience with OpenCV, Open3D, or PCL (Point Cloud Library) for point cloud filtering, spatial segmentation, feature extraction, and 2D/3D coordinate transformations
• Deep experience with PyTorch or TensorFlow
• Proficiency in Scikit-learn for traditional machine learning and statistical data analysis
• Proven experience building low-latency serving infrastructure using NVIDIA Triton Inference Server
• Experience managing end-to-end model workflows on AWS SageMaker
• Familiarity with model quantization, pruning, and target compilers such as ONNX Runtime and TensorRT
• Solid foundation in linear algebra, 3D geometry, coordinate systems, and multi-sensor fusion
• Awareness of modern LLM/multimodal applications
• Basic understanding of RF environment simulation, indoor spatial coverage modeling, or wireless network planning principles
• Experience optimizing or running vision models on iOS devices using CoreML, ARKit, or Metal (preferred)