Role Description Solutions Architects at Databricks lead the growth of the Databricks Data Intelligence Platform. As a team, we have expertise in cloud platforms, data engineering, data analytics, and data science, machine learning, and AI. As a member of our team, you will exercise and grow your expertise in those areas, using open source projects such as Apache Spark™, Delta Lake, and MLflow. This is a customer-facing role, where you will work with customers, your teammates, the product team, our post-sales team, and partners, to identify use cases for Databricks, develop architectures and solutions using our platform, and guide customers through the implementation, to accomplish meaningful outcomes for their businesses. In this process, you will build relationships with our customers, identify and cultivate champions, and establish yourself as a trusted advisor. You will report to a Field Engineering Manager within the Financial Services team at Databricks. - Partner with the sales team to help customers understand how Databricks can help solve their business problems - Provide technical leadership for customers to evaluate and adopt Databricks - Consult on big data architecture, execute proof of concepts for strategic customer projects, and validate integrations with cloud services and other 3rd party applications - Build and present reference architectures, how-tos, and demonstrate applications for customers - Become an expert in, and promote open source projects (Apache Spark™, Delta Lake, MLflow, and Unity Catalog) across developer communities through meetups, conferences, and webinars - Travel to meet with customers in your territory Qualifications - 7+ years in a customer-facing pre-sales role, or consulting role - preferably working with big-data technologies to solve data engineering and big-data analytical challenges - Experience designing and architecting distributed data systems - Comfortable programming in, and debugging, at least one of: Python, SQL, Scala, Java, or R - Experience working with global system integrators, consulting organizations, or financial services technology, or financial services data providers is a positive differentiator - Experience building solutions with public cloud providers such as AWS, Azure, or GCP - Experience with Data Engineering technologies (Ex: Spark, Hadoop, Kafka) - Experience with Data Warehousing technologies (Ex: SQL, OLTP/OLAP/DSS) - Undergraduate degree or higher in an engineering or mathematics discipline such as Computer Science, Applied Mathematics, Electrical Engineering, etc. Benefits - Comprehensive benefits and perks that meet the needs of all of our employees Company Description Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow.

• Serve as the primary product-technical consultant for enterprise urgent care and health system partners. • Lead end-to-end EHR implementation projects, -- including Hl7 and API-based connections -- coordinating internal teams (engineering, product, support) to execute on-time delivery. • Collaborate with product, engineering, and marketing to deliver scalable, innovative solutions and ensure your customers are using Solv tools in the most impactful ways • Build deep, trust-based relationships with senior stakeholders across healthcare organizations. • Design and deliver solutions from scratch, tailoring scalable product roadmaps that align to customer business goals. • Ensure effective cross-functional communication and progress tracking across stakeholders — internally and externally.

Role Description Join a fast-moving AI infrastructure team working on the cutting edge of large-scale ML workloads. This role is ideal for engineers who enjoy solving deep technical challenges in distributed training, multi-GPU systems, and scalable AI inference infrastructure. You will work directly with AI-focused clients, helping them get the most out of modern GPUs (H100, B200, etc.) and ML frameworks such as PyTorch (and JAX in some environments). Responsibilities - Work alongside senior AI and infrastructure engineers building large-scale GPU platforms. - Design and validate production-grade distributed training (primary) and large-scale inference architectures on large GPU clusters, typically tens to thousands of GPUs. - Work hands-on with customers to debug, optimize, and scale ML workloads across multi-node GPU environments. - Act as a technical authority on GPU performance, networking, and schedulers, making trade-offs at scale and translating customer needs into concrete platform requirements. - Collaborate closely with engineering, product, and R&D to influence roadmap decisions based on real-world ML workloads. - This is a hands-on, technical role; you are expected to work directly in customer environments, not only advise at a high level. Qualifications - Hands-on experience designing and operating enterprise-scale, production-grade, multi-node GPU workloads for training (7B+ model size) or inference. - Strong background in distributed deep learning (PyTorch Distributed, DeepSpeed, ...) on GPU clusters. - Deep understanding of GPU architecture and interconnects (H100/A100 class, NVLink, InfiniBand). - Experience with Kubernetes or Slurm. - Experience with performance tuning using GPU profiling and monitoring tools. Requirements This role is not a fit if your experience is limited to single-node training, high-level AI strategy, or non-production research environments. We are looking for engineers and architects who thrive at the intersection of AI workloads and large-scale infrastructure. Benefits - Location: Remote from anywhere in Europe. - Total compensation up to EUR 250k (base + variable / OTE), depending on level and experience.

• Build and maintain solutions using modern programming languages and frameworks, following company coding standards and best practices. • Collaborate with senior developers to continuously improve code quality, maintainability, and scalability. • Enhance existing solutions based on client and stakeholder feedback, optimizing for both performance and user experience. • Conduct thorough testing (unit, integration, and user acceptance) to ensure functionality and reliability. • Support deployments to production environments, monitor performance, and participate in issue resolution. • Create clear technical documentation, including architecture diagrams and user guides. • Partner with cross-functional teams to align solutions with business goals. • Review preliminary designs and provide feedback to improve efficiency, structure, and maintainability.