Siemens Digital Industries is an innovation leader in automation and digitalization. Closely, collaborating with partners and customers, we care about the digital transformation in the process and discrete industries. With our Digital Enterprise portfolio, we provide and encourage companies of all sizes with an end-to-end set of products, solutions and services to integrate and digitalize the entire value chain. Meaningful optimization for the specific needs of each industry, our outstanding portfolio supports customers to achieve greater efficiency and flexibility. We are constantly adding innovations to its portfolio to integrate groundbreaking future technologies.
We're seeking a dynamic and seasoned Scrum Master to join our technology and innovation team. This individual will have a pivotal role in executing agile ceremonies in support of teams designing and creating new technologies by working closely with data scientists, developers, and domain experts.
Position Overview
The Senior Scrum Master provides Agile leadership to multi disciplinary engineering teams delivering complex, large scale software, platform, and system integration solutions. This role requires strong technical fluency and demonstrated experience supporting teams operating in high complexity environments such as cloud engineering, embedded systems, AI/ML, cybersecurity, DevOps, and enterprise grade architectures.
This position carries a specialized focus on integrating Agentic AI tools and intelligent automation into Agile delivery processes. The Senior Scrum Master blends traditional Scrum leadership with a deep understanding of software development, enabling teams to deliver high quality solutions faster and more predictably, supported by AI powered insights and autonomous workflow optimization.
In addition to leading Agile ceremonies and championing team health, this role is responsible for deploying and operationalizing Agentic AI systems to augment engineering workflows, automate lifecycle activities, improve SDLC efficiency, and enhance technical decision making across the program.
The ideal candidate brings a strong foundation in Agile frameworks, engineering workflows, and emerging AI technologies—and is passionate about driving continuous improvement, accelerating team productivity, and advancing innovation through intelligent automation.
This is a role for a technologist Scrum Master hybrid: someone with a deep appreciation for engineering architecture, development pipelines, automation strategies, and modern AI enabled delivery models.
Core Responsibilities:
Agile Leadership in High Complexity Engineering Environments
• Lead and mature Agile execution across software development, platform engineering, DevOps, and cross functional integration teams.
• Apply SAFe frameworks in environments requiring deterministic flow, compliance, and high system reliability.
• Drive alignment between system architecture, sprint commitments, dependency management, and release milestones.
• Facilitate highfidelity team ceremonies, ensuring technical risks, architectural impacts, and integration points are fully surfaced and addressed.
• Remove impediments, anticipate delivery risks, and promote healthy team dynamics.
Agentic AI Integration & Automated Workflow Optimization
• Leverage Agentic AI systems to monitor sprint health, detect bottlenecks, and surface workflow improvement recommendations.
• Implement AIdriven automation to streamline Scrum tasks such as: velocity reports, burndown charts, backlog refinement suggestions, sprint summaries, and release note generation.
• Partner with engineering teams to integrate AI agents into CI/CD pipelines, development tools, and documentation systems.
• Assess the feasibility of AI-generated recommendations, ensuring alignment with engineering best practices.
• Promote responsible AI usage, transparency, and continuous learning across the team.
• Deploy Agentic AI agents capable of autonomously analyzing engineering pipelines, including CI/CD telemetry, merge activity, test coverage, runtime logs, and operational metrics.
Operationalize AI driven automation
o Backlog grooming & technical decomposition
o Story estimation & complexity classification
o Anomaly detection in build/test failures
o Sprint/PI forecasting using historical engineering data
o Intelligent risk surfacing and dependency mapping
• Integrate AI copilots into engineering workflows (IDE coding assistance, automated test generation, architecture evaluations, traceability).
• Validate AI recommendations through engineering rigor, ensuring outputs meet security, compliance, and reliability standards.
Technical Collaboration & Engineering Alignment
• Use software development knowledge to translate technical impediments, risks, and dependencies into actionable plans.
• Collaborate with architects and developers to ensure that sprint goals are well understood and technically realistic.
• Support teams with AI-assisted code reviews, automated test insights, or technical documentation generation (when appropriate).
• Champion DevOps, automation, and engineering excellence within the Agile process.
• Use systems and software development expertise to collaborate with architects, developers, SREs, testers, and DevOps engineers.
• Partner with engineering leadership to translate architectural roadmaps, epics, and technical debt into actionable and groomed backlogs.
• Proactively identify systemic constraints across microservices, pipelines, integrations, or infrastructure layers.
• Facilitate resolution of technical blockers across environment provisioning, build systems, APIs, data flows, and deployment pipelines.
SDLC Optimization, Tooling, & Delivery Architecture
• Drive improvements across the full software lifecycle: coding, branching strategy, automated testing, security scanning, packaging, deployment, and observability.
• Enhance delivery consistency using AIsupported valuestream mapping and configurationtime analytics.
• Partner with DevOps and Cloud Engineering to instrument pipelines for traceability, telemetry capture, and realtime sprint health monitoring.
• Influence architecture decisions by surfacing databacked insights on performance, reliability, risk, and delivery predictability.
AIAugmented Metrics, Dashboards & Reporting
• Own key Agile performance metrics (cycle time, throughput, WIP, predictability) and enhance them with AIpowered analytics.
• Use AI-driven retrospectives to identify long-term patterns and guide process experimentation.
• Develop dashboards and intelligent reporting that increase visibility and foster data-informed decisionmaking across the program.
Build and maintain advanced metrics pipelines using AI to augment traditional Agile KPIs:
o Cycle time variance detection
o Engineerlevel flow efficiency
o Build/test failure clustering
o Automated dependency risk graphs
o Predictive velocity forecasts
• Deliver technical insights and readiness assessments to engineering leadership, program management, and product stakeholders
Stakeholder Communication & Alignment
• Maintain alignment between team activities, product strategy, and organizational objectives.
• Communicate progress, risks, and key insights to leadership in clear, data-backed updates.
• Foster a culture of collaboration across engineering, product, QA, and business stakeholders.
• Support technical readiness reviews, compliance milestones, and major release events.
• Champion a culture of engineering excellence, automation, and intelligent augmentation
Required Qualifications:
• Engineering background (Full Stack Software Engineer, DevOps, SysEng, SRE, QA Automation, or similar).
• 5+ years of experience as a Scrum Master, Agile Lead, or Technical Program Lead within engineeringheavy or missioncritical environments.
• Strong working knowledge of software engineering practices: architecture, APIs, microservices, build systems, test frameworks, CI/CD, cloud platforms, or embedded systems.
• Handson experience with AI/ML tooling, automation frameworks, copilot or Agentic AI systems.
• Demonstrated ability to support technically complex programs involving multiteam integration.
• Strong foundational understanding of software development concepts (SDLC, APIs, cloud, version control, CI/CD, testing).
• Fluency in Jira, AWS, Azure DevOps, GitLab, GitHub Enterprise, or similar engineering lifecycle tools.
• Certifications: CSM, PSM, SAFe, ICAgile, or equivalent.
Preferred Qualifications:
• Experience designing or integrating AI agents into engineering workflows.
• Experience in cloud platforms (Azure, AWS, GCP) or containerorchestrated systems (Kubernetes).
• Strong understanding of DevOps, cybersecurity controls, and automated compliance.
• Background as a developer, QA engineer, or technical analyst.
• Experience in DevOps, cloud technologies, or AIenhanced engineering practices.
• Ability to translate data insights into strategic team or process improvements.
Why us?
Working at Siemens Software means flexibility - Choosing between working at home and the office at other times is the norm here. We offer great benefits and rewards, as you'd expect from a world leader in industrial software.
A collection of over 377,000 minds building the future one day at a time in over 200 countries. We're dedicated to equality, and we welcome applications that reflect the diversity of the communities we work in. All employment decisions at Siemens are based on qualifications, merit, and business need. Bring your curiosity and creativity and help us shape tomorrow!
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