Zürich Metropolitan Area
I’m a Cloud and MLOps engineer with hands-on experience deploying and scaling infrastructure in AWS and Azure. I’ve built systems from scratch that power real-time analytics, IoT device fleets, and AI model deployments. At UBS, I contributed to the cloud infrastructure supporting the AI Development Studio, helping 100+ internal users access GPU-backed Kubernetes environments while reducing vendor costs. In a previous role, I built CI/CD pipelines, automated firmware deployments, and processed over a million daily sensor readings through AWS Kinesis Data Streams, IoT Core, serverless functions and a number of database services for optimal and cost effective data pipelines. I enjoy solving infrastructure problems, improving reliability, writing highly scalable backends, and learning from experienced teams. I’m currently working toward my next AWS certification and looking for a high performance engineering team where I can grow and contribute.
Building and supporting cloud infrastructure powering internal AI development environments
Helping various clients implement their visions, un-vibe their code, and ship quality software directly to the clients. Reach out at: [email protected]
Designed end-to-end AWS infrastructure supporting a growing IoT startup. Managed 100+ IoT devices using MQTT and OTA firmware updates Built CI/CD workflows with GitHub Actions and Ansible Created real-time analytics pipeline processing 1M+ sensor events daily
Engineered distributed reinforcement learning training infrastructure using CUDA optimization across 5 networked university workstations, achieving 2.5x training speedup for TensorFlow-based RL agent in hydrogen plant optimization system Successfully delivered hydrogen facility monitoring system that reduced alarm flooding by 55% in production deployment Created user-friendly ML pipeline UI enabling facility operators to understand and act on system recommendations