• Build and evolve an internal developer platform with clear self-service workflows ("golden paths") that makes it easy for Clever engineers to build, deploy, and scale well-architected applications. • Define and maintain standards, templates, and automation that help teams ship quickly and safely without sacrificing reliability, security, or compliance. • Collaborate with product engineering teams to understand friction points, gather requirements, and iterate on platform capabilities as an internal product. • Provide observability primitives (logging, metrics, tracing, alerting) and opinionated defaults so teams can operate services effectively with minimal toil. • Implement multi-region capabilities, including repeatable environment provisioning, resiliency patterns, and fast region failover. • Lead resiliency initiatives and implement reliability engineering practices to meet or exceed 99.98% uptime for critical services. • Model and reinforce Clever's engineering culture and practices, including collaboration, operational excellence, and continuous improvement.

• Du betreust Kundenprojekte von Anfang bis Ende • Du entwickelst Softwarelösungen von Front- bis Backend • Du nutzt modernste Technologien und lernst durch verschiedene Projekte die vielen Facetten der .NET- Entwicklung kennen • Du übernimmst Verantwortung und lernst verschiedenste Branchen kennen

• This role focuses on building agentic, AI-powered experiences that help people build computer vision solutions with Roboflow end-to-end, across both in-app experiences and external integrations. • You’ll also contribute to the core product surfaces agents rely on, especially Workflows, our computer vision workflow and pipeline builder, and our Deployments offering in general. • This includes tightening the path from prototype to production by filling in missing functionality and improving reliability, tooling, and discoverability. • You’ll have a lot of freedom to advocate for what should be built next, and you’ll contribute to product and strategy decisions. • If you need a rigid list of tasks spelled out in a multi-month roadmap, this role probably won’t be a good fit.

• Build and maintain scalable systems and infrastructure for deploying and serving ML models. • Design low-latency, fault-tolerant model inference systems using Amazon SageMaker. • Implement safe deployment strategies like blue/green deployments and rollbacks. • Create and manage CI/CD pipelines for ML workflows. • Monitor model performance and system health using AWS observability tools (e.g., CloudWatch). • Develop internal tools and APIs to help ML teams deploy and monitor models easily. • Collaborate with ML engineers, data scientists, and DevOps to productionize new models. • Participate in code reviews, system design, and platform roadmap discussions. • Continuously improve deployment reliability, speed, and usability of the ML platform.