AI Engineer

About this role

The Opportunity We are looking for an AI Engineer to join our Ekahau team. Ekahau enables IT professionals to takecontrol of their Wi-Fi, making it easier than ever before to proactively monitor, maintain, and optimizetheir networks. Organizations of every size—including the world’s biggest brands and events—use oursoftware and hardware products for full Wi-Fi lifecycle management and the highest levels ofperformance and connectivity. Our award-winning design, site survey, and troubleshooting solutionscreate fast, reliable networks that businesses can trust for their mission-critical Wi-Fi needs. In this role, you will work as part of our research team, exploring and pioneering innovative technologiesand methodologies related to wireless communication systems planning, optimization, spatial mapping,and network troubleshooting. You will bridge cutting-edge spatial AI (LiDAR/camera floorplan extractionand 3D point cloud analysis) and modern AI, ML, and LLM approaches with high-performance edge andcloud infrastructure (AWS SageMaker, NVIDIA Triton). By conducting experiments, building proof-of-concepts, and translating theoretical concepts into production-grade systems, you will directly shapehow indoor environments are captured, analyzed, and optimized for global connectivity. Expectations for Success 3D Spatial & Computer Vision Engineering: Design, build, and maintain real-time 3D spatial processing pipelines, leveraging sensor fusion (LiDAR, camera feeds, spatial telemetry) for pointcloud filtering, segmentation, and 3D layout analysis. Computer Vision & Layout Detection: Develop computer vision models and downstream post-processing algorithms to extract structural features, recognize building geometry, and generateprecise 2D/3D floorplans from raw visual and spatial data. Research to Production: Translate research findings, algorithmic prototypes, and modern AI/ML/LLM concepts into high-performance, maintainable production code in Python, taking direct ownership of core product implementations. Scalable Cloud Inference Architecture: Architect, deploy, and manage multi-model inference pipelines on AWS SageMaker and NVIDIA Triton Inference Server, ensuring low-latency processing and reliable high-throughput serving. MLOps, Data Engineering & System Observability: Build end-to-end data and MLOps pipelines—encompassing synthetic data generation, active annotation, dataset versioning, continuous integration/deployment (CI/CD), and real-time telemetry—to continuously evaluate,deploy, and monitor model performance, latency, and spatial accuracy. Cross-Functional Technical Collaboration: Work directly alongside software engineering, research, and product management teams to transition prototype features into scalable, market-ready releases. Requirements Software Engineering: Production-level mastery of Python alongside working knowledge of C++or Swift, emphasizing clean code, modular design, and execution speed. Computer Vision & 3D Spatial Processing: Hands-on experience with OpenCV, Open3D, or PCL(Point Cloud Library) for point cloud filtering, spatial segmentation, feature extraction, and2D/3D coordinate transformations. ML & Deep Learning Frameworks: Deep experience with PyTorch or TensorFlow, alongside proficiency in Scikit-learn for traditional machine learning and statistical data analysis. High-Throughput Cloud Serving: Proven experience building low-latency serving infrastructurusing NVIDIA Triton Inference Server and managing end-to-end model workflows on AWSSageMaker. Model Optimization & Acceleration: Familiarity with model quantization, pruning, and target compilers (e.g., ONNX Runtime, TensorRT) to hit production latency targets. Applied AI & Domain Math: Solid foundation in linear algebra, 3D geometry, coordinate systems, multi-sensor fusion, and awareness of modern LLM/multimodal applications. Preferred Technical Qualifications Wireless Domain Knowledge: Basic understanding of RF environment simulation, indoor spatia

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