Solutions Architect

At ClearML, our mission is to make AI infrastructure effortless across every phase of the AI lifecycle — from building and training models to large-scale production. Trusted by more than 2,100 organizations worldwide, AI builders and IT teams use our platform to power everything from early-stage R&D to enterprise-grade AI pipelines. We’re growing quickly and looking for curious, driven individuals who are excited to shape the future of AI and the infrastructure that powers it. Our customers tackle some of the world’s most important challenges — revolutionizing healthcare, discovering new medicines, securing global finance, protecting national security, and preserving our planet’s ecosystems. About the Role We are seeking a Solutions Architect who combines deep technical expertise with strong communication and problem-solving skills. The ideal candidate has a solid engineering background — ideally as a hands-on developer or DevOps engineer — and is eager to bridge the gap between technology and business outcomes. As a Solutions Architect at ClearML, you will work directly with enterprise customers to design, deploy, debug and scale AI infrastructure solutions using ClearML’s platform. You’ll collaborate with data scientists, MLOps and Devops teams, as well as IT engineers to ensure success across the AI lifecycle — from infrastructure setup, experimentation and model training to deployment and monitoring. This is a hands-on role that requires technical excellence, clear communication, and the ability to operate effectively in a fast-paced, dynamic environment. Responsibilities Engage with enterprise customers to understand their technical challenges, architecture, and goals. Design and implement scalable, secure, and efficient ClearML-based solutions on Kubernetes and cloud/hybrid environments. Lead technical workshops, proofs of concept (POCs), and architecture reviews. Present complex technical concepts in a simple, business-oriented manner. Collaborate closely with Product, Engineering, and Customer Success teams to ensure successful adoption and expansion. Provide guidance and best practices for deploying AI workloads on Kubernetes clusters. Troubleshoot customer environments and provide hands-on support during deployment and onboarding. Contribute to documentation, solution templates, and reusable technical assets. Requirements Strong Hands-on expertise in Kubernetes — deep understanding of architecture, scaling, and operations. Strong communication skills in English, both written and verbal — able to explain complex topics clearly and effectively. Prior experience as a software engineer, DevOps engineer, or MLOps engineer, with the desire to grow into a customer-facing Solutions Architect role. Proven ability to design and deploy solutions in cloud or on-premise environments (AWS, GCP, Azure, or hybrid). Experience with Docker, REST APIs, and Python-based solutions. Comfortable working in a fast-paced, dynamic environment with minimal supervision. Strong presentation skills — able to engage both technical and business audiences. Willingness to work European and U.S. hours as needed for customer collaboration. Team player with a proactive attitude and strong sense of ownership. Preferred Qualifications Experience in AI/ML infrastructure, MLOps, or data engineering. Familiarity with distributed systems, model training workflows, or inference serving. Previous experience in pre-sales, customer success, or technical consulting roles. Why Join ClearML? ClearML is on a mission to make AI infrastructure effortless — from early experimentation to large-scale, production-grade systems. Trusted by 2,100+ organizations worldwide , our platform powers real-world, mission-critical AI workloads. Fully Remote & Global – Work from anywhere with a distributed team of top-tier engineers.