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You are a member of the System Architects team within global IT Architecture. Together with your colleagues from the Domain and Foundation Architecture teams, you are responsible for designing Solution Architectures.
As a System Architect within the ML & AI ART, you are responsible for defining and aligning the end‑to‑end system architecture for enterprise AI and ML solutions. Your objective is to ensure the ART delivers scalable, secure, and reusable AI capabilities aligned with enterprise architecture principles, target architectures, and business priorities, while enabling efficient and high-quality delivery across teams.
YOUR CHALLENGE
- You design and are responsible for the Solution Architecture of applications and AI platform components in delivery
- You ensure that Solution Architectures in delivery adhere to architecture standards and are aligned with the target architectures of the domain
- You initiate architecture decisions, document them as Architecture Decision Records (ADR) and have them approved by the relevant architecture boards
- You advise and coach software engineers on the implementation of Solution Architectures and ensure technical feasibility
Within the ML & AI ART in collaboration with the Lead Architect, you:
- Apply and refine the ART‑level target architecture within team and initiative contexts
- Drive hands‑on architectural design for AI and ML solutions within the teams, balancing consistency while addressing delivery needs
- Support the delivery of key ART initiatives (e.g. the AI automation orchestration layer) by translating high‑level architecture into concrete solution designs
- Ensure alignment and integration with enterprise platforms, data capabilities, security standards, and MLOps services
- Identify architectural risks, dependencies, and technical debt at team and solution level, and escalate them proactively
- Contribute architectural input to PI Planning, backlog refinement, and roadmap discussions from a delivery and feasibility perspective
- Act as the primary architectural contact for assigned teams, while aligning closely with the Lead Architect and other System Architects
YOUR PROFILE
- At least 3 years of experience as a Senior Architect in complex IT environments
- At least 5 years of experience as a Lead or Senior Software Engineer, with hands-on responsibility including practical experience with AI/ML solutions and platforms
- Responsible for solution architectures: from design and implementation to operation
- Ability to identify options for solution architectures and make trade-off architecture decisions
- Collaboratively develop and coordinate solutions with business and technology stakeholders and architecture colleagues
- Proactive mindset, able to work under pressure and meet deadlines
- Practical experience designing and delivering AI and ML solutions in scalable enterprise platforms
- Strong understanding of AI/ML solution patterns, data pipelines, orchestration, and platform integration
- Ability to translate ART- and domain-level target architectures into practical, team-level solution designs
- Experience working across multiple teams while maintaining architectural alignment and consistency
- Ability to identify architectural risks and technical debt early and address them constructively
- Strong communication skills, with the ability to clearly explain architectural topics to both technical and non-technical stakeholders
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a comparable field
- Additional certifications are an advantage (e.g. SAFe, cloud, architecture, or AI/ML-related certifications)
- English: business fluent
- Solid foundation in software and system architecture, including modern architectural styles such as event-driven architectures, microservices, and modular monoliths
- Strong understanding of distributed systems, APIs, and platform integration patterns
- Practical familiarity with AI/ML platforms and ecosystems, including data pipelines, orchestration, and model lifecycle concepts
- Experience working with CI/CD pipelines, DevOps practices, and operational considerations in production environments
- Ability to work effectively in agile and SAFe environments using common collaboration and delivery tooling
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