AI / GenAI Solutions Architect (AWS) (f/m/d)
About the Role
Location Germany Bayern Erlangen
1. Country: PORTUGAL
2. State/Province/County: Lisboa
3. City: Lisbon
4. Country: GERMANY
5. State/Province/County: Berlin
6. City: Berlin
Remote vs. Office Hybrid (Remote/Office) Company Siemens Energy Global GmbH & Co. KG Organization EVP Global Functions Business Unit Strategic Procurement Full / Part time Full-time Experience Level Mid-level Professional A Snapshot of Your Day We are looking for a Cloud AI / ML & GenAI Architect (m/f/d) to design and govern the cloud architecture that enables our enterprise AI and data platform. The role focuses on establishing scalable AWS foundations for AI, machine learning, and generative AI applications, while ensuring alignment with the organization’s broader data platform, governance, and engineering standards.You will work closely with data engineers, technical data architects, AI engineers, and external partners to ensure that our cloud environment reliably supports GenAI, Agentic AI, and Retrieval-Augmented Generation (RAG) use cases. A key part of the role is defining reusable architecture patterns, platform components, and operational frameworks that allow teams to build and deploy AI solutions efficiently and securely.How You’ll Make an Impact
7. Define and maintain the cloud architecture for AI/ML and GenAI workloads on cloud infrastructure (preferred AWS).
8. Design and implement scalable platform components and reusable reference architectures for AI and data teams.
9. Establish MLOps / GenAIOps frameworks for model deployment, monitoring, and lifecycle management.
10. Define secure and scalable AWS environments, including account structure, IAM models, networking, and guardrails.
11. Ensure cloud solutions meet enterprise standards for security, reliability, performance, and cost efficiency.
12. Collaborate with data engineering and architecture teams to ensure the platform supports RAG pipelines, agentic systems, and advanced AI applications.
What You Bring
13. Strong experience architecting AI/ML and GenAI platforms on AWS or similar cloud tool
14. Advanced Python skills for cloud services, automation, and AI/ML workloads
15. Experience designing serverless, microservice, and event-driven architectures
16. Experience implementing MLOps / GenAIOps pipelines and model lifecycle management
17. Solid understanding of modern data architectures and integration with enterprise data platforms
18. Ability to define architecture standards, reusable components, and platform governance