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 Dev Ops/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.

• Drive, design and own development ofplatformto deliver data based on actionable insights
• End to end responsibility for the technical requirements, design, development, integration and verification of platform-based solution that uses machine learning to analyze large datasets
• Select,designand implement suitable cloud databases for slow and fast data storage with emphasis on scalability, reliability and performance
• Optimize the ML algorithms to ensure high performance and reliability
• Develop API strategy, APIsanddesign pathways to integrate with customer systems
• Create and maintain a CI/CD infrastructure that supports the scaling of the AWS-based platform
• Provide and maintain tooling, templates, and best practices to standardize development, deployment, and ML workflows
• Monitor, troubleshoot, and continuously improve production systems, CI/CD workflows, and ML pipelines, with a focus on performance, security, and cost
• Actively participate in software development and integration of real time solutions
• Close collaboration with the team on the development process, including defining goals and ensuring milestones delivery in a high cross-functional capacity as per customer’s needs

• MSc or advanced degree in a relevant field with 5+ years of experience in MLOps, ML model deployment, and cloud infrastructure
• Strong hands-on experience with AWS services (e.g. EC2, S3, IAM, ECR, ECS/EKS, Sage Maker)
• Proven experience designing and maintaining CI/CD pipelines (Git Hub Actions, Git Lab CI, Jenkins, or similar)
• Strong proficiency with Linux, scripting, and automation (Bash, Python)
• Experience with containerized environments (Docker; Kubernetes is a plus)
• Practical experience supporting machine learning workflows, including training, model versioning, and deployment
• Strong problem-solving and communication skills, with the ability to document systems and workflows clearly

• Experience with Infrastructure as Code tools (Terraform, Cloud Formation, or equivalent)
• Experience with embedded real times system is a huge plus