AI Engineer

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 spatialcoverage modeling, or wireless network planning principles.

• Mobile Edge Integration: Experience optimizing or running vision models on iOS devices

(CoreML, ARKit, Metal).

About

Ookla, an Accenture company, is a global leader in connectivity intelligence that brings together the trusted expertise of Speedtest®, Downdetector®, Ekahau®, and RootMetrics® to deliver unmatched network and connectivity insights. By combining multi-source data with industry-leading expertise, we transform network performance metrics into strategic, actionable insights.

Our solutions empower service providers, enterprises, and governments with the critical data and insights needed to optimize networks, enhance digital experiences, and help close the digital divide. At the same time, we amplify the real-world experiences of individuals and businesses that rely on connectivity to work, learn, and communicate. From measuring and analyzing connectivity to driving industry innovation, Ookla helps the world stay connected.

About Accenture

Accenture helps the world’s leading enterprises reinvent by building their digital core and unleashing the power of AI to create value at speed for organizations across industries. Our strategy is to be the reinvention partner of choice for our clients and lead in the safe, widespread adoption of AI, and to be the most client focused, AI-enabled, great place to work in the world. We bring together the talent of our approximately 799,000 people with proprietary assets and platforms, deep process and industry expertise, and leading ecosystem relationships to deliver end-to-end solutions and measurable outcomes at scale. Through our Reinvention Services, we offer broad expertise across Cybersecurity, Digital Core, Finance, Industry and Enterprise, Song, Supply Chain and Engineering, and Talent, with advanced capabilities in AI and Data, Industry and Process, and Technology. We serve approximately 9,000 clients and generated approximately $70 billion in FY25 revenue. Visit us at.

Compensation Range

Ookla provides a range for the base pay. Factors that may be used to determine your actual pay may include your specific job related knowledge, skills, experience, and geographic location. The salary compensation for this role is x - x. Individual pay within the compensation range for this business unit specific role is determined based on a variety of factors including experience, scope of the role, capabilities to perform the role, education and training, as well as business and company performance.

Originally posted on Himalayas