Senior MLOps/DevOps Engineer

Company Description 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 who is genuine passionate about innovation, cloud infrastructure, and building robust systems end-to-end. If you thrive in a dynamic startup environment, take ownership of AWS environments, and have the coding chops to bridge the gap between machine learning models, backend APIs, and embedded systems, we’d love to meet you. 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. 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 MSc or advanced degree in a relevant technical field. Direct experience deploying to embedded real-time systems or edge devices. Addition Stock Options 401(k) Paid time off Health insurance Dental insurance Vision insurance