Senior DevOps/MLOps Engineer

Senior DevOps / MLOps Engineer Algorized is a VC-funded Silicon Valley deep-tech company with Swiss roots building edge-AI models that give robots real-time human awareness using existing wireless sensors - enabling safer human-machine co-presence. As we continue to scale rapidly, we are looking for a Senior DevOps/MLOps Engineer for our office at Etoy, Switzerland who is genuine passionate about innovation, product development and building robust systems end-to-end. If you thrive in dynamic startup environment, take ownership, and know to seamlessly connect backend, frontend, and embedded systems, we’d love to meet you. LOCATION On-Site/Campbell, CA EMPLOYMENT TYPE Full Time Responsibilities AWS & ML Infrastructure: Build, own, and scale the end-to-end AWS cloud infrastructure (including compute, container orchestration, and provisioning databases for both real-time serving and large-scale ML data storage). MLOps Pipelines: Provide and maintain tooling, templates, and best practices for ML workflows, including model versioning, automated training pipelines, and serving endpoints (e.g., using SageMaker). CI/CD & Automation: Create and manage comprehensive CI/CD pipelines to support fast, reliable deployments of our cloud platform and ML services. System Integrations: Write integration code and APIs to seamlessly connect our ML cloud environments with customer systems and edge/embedded devices. Monitoring & Reliability: Monitor, troubleshoot, and continuously improve production systems with a strict focus on system performance, security, and AWS cost-optimization. Cross-Functional Collaboration: Actively participate in the integration of real-time solutions, working closely with data scientists and embedded engineers to deliver on customer needs. Qualifications Minimum Requirements MSc in Computer Science, Engineering, or a relevant field (or equivalent practical experience) with 5+ years of experience in DevOps, Cloud Engineering, or MLOps. Deep, hands-on expertise with AWS services (EC2, S3, IAM, ECR, ECS/EKS, SageMaker). Strong programming proficiency in Python and Bash, combined with working knowledge/experience in C/C++ to collaborate effectively with our embedded engineering teams. Strong proficiency in writing Infrastructure as Code (Terraform, CloudFormation, or equivalent). Proven experience designing and maintaining CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, or similar). Extensive experience with containerized environments (Docker) and container orchestration (Kubernetes/EKS). Practical experience supporting machine learning deployment workflows and model serving. Strong problem-solving skills with the ability to document systems and infrastructure clearly. Preferred Requirements Direct experience deploying to embedded real-time systems or edge devices.