We are looking for a seasoned MLOps Engineer to lead the deployment and optimization of Edge AI solutions across industrial sites. The role focuses on building end‑to‑end pipelines that integrate hardware, software, and data science to deliver high‑performance inference on embedded GPUs. Responsibilities include model optimization with TensorRT and ONNX, GPU benchmarking, CI/CD automation, and large‑scale orchestration using Kubernetes and fleet management tools. The ideal candidate has a strong background in MLOps, Edge computing, and deep learning, and is comfortable working in a fast‑moving startup studio environment.

We are seeking an experienced MLOps Engineer – Edge AI Specialist to design and deploy on‑premise inference infrastructure for industrial vision systems. The role involves building hybrid edge/cloud architectures, optimizing deep learning models for embedded GPUs, and maintaining CI/CD pipelines for edge deployment. The ideal candidate will have 3–5 years of experience in MLOps, edge computing, and GPU performance profiling, with strong skills in Python, Docker, Kubernetes, and monitoring tools. This position offers the chance to work in a fast‑moving startup studio focused on scalable AI ventures.

We are seeking an experienced MLOps Engineer – Edge AI Specialist to lead the design and deployment of on‑premise inference infrastructure for industrial vision systems. The role involves architecture of hybrid edge/cloud solutions, model optimization for embedded GPUs, CI/CD pipeline development, fleet management, and compliance with GDPR. The ideal candidate has 3‑5 years of production experience, expertise in TensorRT, ONNX, Kubernetes, and Python, and a strong background in real‑time analytics and GPU performance profiling.