DevOps Engineer

For a large organization within the financial services sector, we are looking for an experienced Senior Dynatrace Engineer – AI Observability . The organization is strongly focused on scaling up its AI initiatives and wants to further professionalize its observability landscape. New AI solutions based on technologies such as Databricks, AI Foundry and cloud-native workloads require reliable insights into performance, availability, end-to-end chain visibility and fast root cause analysis. Observability is crucial to ensure that AI and cloud environments can grow in a secure, scalable and manageable way. Role Description As Senior Dynatrace Engineer for AI Observability , you will help the organization further implement, standardize and scale Dynatrace within AI and cloud-native environments. You will work closely with monitoring teams, AI teams and other stakeholders to implement observability patterns, define reusable standards and help teams use Dynatrace more effectively and independently. This role requires more than technical support alone. You will contribute to vision, standardization, knowledge transfer and demonstrating concrete value to technical teams, business stakeholders and management. Responsibilities Your responsibilities include: Advising and supporting the setup of AI Observability for Databricks, AI Foundry and related AI workloads. Working hands-on with monitoring and AI teams to implement observability patterns for cloud-native environments. Defining scalable and reusable standards for dashboards, tagging, logging, tracing, alerting and root cause analysis. Supporting concrete implementations within Dynatrace. Translating telemetry data into practical insights for stakeholders, technical teams and business teams. Contributing to knowledge transfer, demos and best practices. Accelerating adoption and effective use of Dynatrace within the organization. Supporting broader observability-related topics. Contributing to a mature, standardized and future-proof observability approach. Objective of the Assignment The objective of this assignment is to enable broader, more consistent and more professional use of Dynatrace within AI and cloud environments. The focus is on: Applying Dynatrace for AI Observability. Accelerating adoption within the organization. Establishing standards for teams. Improving monitoring, logging, tracing, alerting and root cause analysis. Creating reusable patterns that allow teams to work faster and more independently. Demonstrating the value of observability to stakeholders. Required Experience and Knowledge The ideal candidate has: Deep hands-on experience with Dynatrace in complex enterprise environments. Strong knowledge of AI Observability. Experience with cloud-native monitoring. Knowledge of observability for modern data and AI platforms. Experience with application monitoring, distributed tracing, logging, metrics, dashboards and alerting. Experience with root cause analysis within complex IT landscapes. Knowledge of Databricks, AI Foundry or similar AI/data platforms is a strong plus. The ability to translate observability capabilities into practical value for teams and stakeholders. Experience with standardization, platform adoption and knowledge transfer. Competencies The candidate demonstrates the following competencies: Strong analytical skills. Hands-on mentality combined with strategic insight. Strong communication skills. Ability to explain technical complexity clearly and practically. Advisory skills towards both technical and non-technical stakeholders. Proactive and independent way of working. Focus on standardization, scalability and reusability. Collaborative mindset within multidisciplinary teams. Quality-driven and focused on continuous improvement. Candidate Profile This assignment is well suited for a senior observability engineer with deep Dynatrace expertise and an affinity with AI, data and cloud-native platforms. You are not a consultant who only answers individual technical questions, but someone who actively helps build a mature observability approach. You help teams with concrete implementations, develop standards, transfer knowledge and ensure that observability structurally adds value to AI and cloud initiatives within a complex enterprise environment.