• Lead the deployment of sophisticated AI models and applications using state-of-the-art tools and platforms. • Design and Develop AI solutions specifically harnessing the power of LLMs. • Work with industry-standard LLM APIs to inject cutting-edge AI functionalities into broader applications using your experience with cloud platforms like AWS and Azure. • Create models that drive business outcomes and make iterative improvements based on data-driven insights. • Ensure deployed models operate at peak efficiency, requiring a vigilant approach to performance monitoring and adjustment. • Deploy models within an Agile development framework, showcasing flexibility, rapid iteration, and a commitment to constant improvement. Proactively collaborate within the Applied AI team and cross-functionally with other development teams, product and business stakeholders to iterate on ideas, solve problems, and achieve shared goals.

• Ensure AI tools are consistently used and integrated into daily work, not just explored; Identify and remove barriers to adoption • Deliver enablement at scale, including role-based learning journeys, toolkits, office hours/coaching, and a champion network in partnership with training and operations • Help establish AI COE framework to promote responsible AI use, best practices, training and enablement programs, success metrics, and communication • Partner with training and operations teams to develop role-based enablement across the enterprise (sales, service, ops, leadership) • Equip teams to effectively use AI platforms, Copilot tools, AI agents, and prompt & agent libraries • Provide ongoing support through office hours, coaching, and trainings materials • Reinforce expectations for when and how AI should be used; Ensure responsible AI usage by incorporating governance and guardrails into training, communications, and playbooks • Partner with business and operations teams to roll out AI capabilities and integrate them into daily work. Develop change management strategies and educate teams on AI benefits. • Identify where AI can meaningfully improve workflows, quality, and speed; Help define roles of AI (automate, augment, or escalate) vs human • Ensure AI usage is clear, repeatable, and consistent to shift from manual to AI-assisted workflows • Understand pain points and translate needs into clear, actionable AI use cases; Identify where AI can deliver meaningful workflow improvements and practical impact • Bring structured recommendations to inform prioritization and sequencing of AI efforts • Act as a bridge between business users and AI/product teams to ensure solutions reflect real needs, are adopted, and deliver value; Provide insight into what will drive adoption and proactively identify where solutions are likely to stall • Support business teams in owning outcomes and driving adoption; Ensure success metrics are defined and use data to continuously refine prioritization and enablement strategies • Gather feedback from users on what’s working, where friction exists, and where trust is low • Partner with engineering to improve usability and effectiveness of AI solutions • Continuously refine enablement materials, guidance, and approach • Enable acceptable AI usage, citizen development, and sharing & reuse of AI tools • Track and report AI impact • Share success stories, use cases, best practices, and lessons learned to accelerate adoption across teams.

Your role at DynatraceAs a Senior Software Engineer, you will build features at the heart of Dynatrace Intelligence, our agentic AI platform. You'll work in a mixed team of senior software engineers and data scientists, tackling challenges across backend services, AI integrations, and LLM tool integrations and MCPs. Your responsibilities: - Develop features for the agentic operations system Dynatrace Intelligence. - Contribute to the backend service providing the core agentic AI functionality, as well as integrating service APIs and leveraging the provided data in AI powered applications. - Ensure and improve the quality of agentic AI applications (e.g. regression testing). - Collaborate with other developers and product management to integrate feedback and create value for Dynatrace customers. What will help you succeed - 5+ years of professional experience as a software engineer. - 3+ years of professional experience in Python and Java software development. - Experience with CI/CD tooling (e.g. Jenkins). - Beneficial skills: -Experience with microservices (e.g. APIs, Docker, K8s) -Experience with cloud environments (AWS, Azure) -Experience with project specific python frameworks such as Django, FastAPI, FastMCP and Langchain -Experience in developing data applications (e.g. data modelling, SQL based data querying) Why you will love being a Dynatracer - Dynatrace is a leader in unified observability and security. - We provide a culture of excellence with competitive compensation packages designed to recognize and reward performance. - Our employees work with the largest cloud providers, including AWS, Microsoft, and Google Cloud, and other leading partners worldwide to create strategic alliances. - You'll get to work at the forefront of innovation with Dynatrace Intelligence—the industry's first agentic operations system. Bringing together deterministic and agentic AI, it helps teams understand what's happening, why it matters, and what to do next— automatically. - Over 50% of the Fortune 100 companies are current customers of Dynatrace. Compensation and RewardsWe offer attractive compensation packages and stock purchase options with numerous benefits and advantages. Due to legal reasons, we are obliged to list a salary range for this position, which is €61,000 up to €92,000 gross per year based on full-time employment (38.5 h/week). We’ve listed the salary range for transparency, but if your experience and skills bring unique value, we’d still love to hear from you—please apply even if you’re outside the range. Equal Employment OpportunityDynatrace provides equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, or any other protected characteristic. We actively foster an inclusive workplace that celebrates differences and promotes accessibility, collaboration, and growth for all.

• Design, build, and deploy end-to-end AI/ML and agent-based systems, from problem definition and model development to production deployment, monitoring, and continuous improvement to solve business problems, and automate enterprise and scientific tasks. • Focus on simulating human learning activities, improving system performance through data analysis, and developing deep learning frameworks and systems. • Collaborate with scientists to build robust data pipelines and ensure high-quality training data. • Design scalable, reliable services on major cloud platforms; strong CI/CD, observability, and operational excellence. • Translate customer requirements to business solutions using data pipelines and statical models. • Build & maintain scalable ML infrastructure, including training pipelines, feature stores, and model serving systems. • Contribute to MLOps best practices, including CI/CD for ML, model versioning, and A/B testing frameworks. • Create exploratory analysis, model design & training, validation, feature engineering, production handoff to drive business optimization. • Responsible for constructing, studying, and training algorithms that learn from complex, high-dimensional data to uncover patterns and develop practical predictive models and applications. • Document architectures, experiments, and results clearly for technical and non-technical stakeholders to support current work and any retraining for the future. • Data, model, and agent pipeline engineering (e.g., workflow orchestration, model lifecycle management, automated retraining/rollouts). • Orchestration and integration across components (agent frameworks, containers, web services/APIs, distributed systems). • Develop and integrate intuitive Copilot experiences into existing tools to provide real-time, AI-driven assistance and insights to team members.