AI Solutions & Architecture Lead

Here at Siemens, we take pride in enabling sustainable progress through technology. We do this through empowering customers by combining the real and digital worlds. Improving how we live, work, and move today and for the next generation! We know that the only way a business thrives is if our people are thriving. That’s why we always put our people first. Our global, diverse team would be happy to support you and challenge you to grow in new ways. Who knows where our shared journey will take you?

Transform the everyday with us!

We are looking for an AI Solutions & Architecture Lead. This position will be based in Wendell, NC, with remote work considered for highly qualified candidates.

The AI Solutions & Architecture Lead serves as the primary technical authority within the AI Excellence team and is responsible for translating business opportunities into scalable, secure, and effective AI solutions.

The role combines solution architecture with hands-on technical execution . You will evaluate technologies, design solution approaches, develop prototypes, establish reusable patterns, support implementation, and provide technical direction to internal IT teams and third-party delivery partners.

You should be comfortable moving from a whiteboard architecture discussion to directly working with AI platforms, APIs, data, integrations, agents, prompts, and prototypes.

You’ll make a difference by:

• AI solution architecture – Translate business requirements and use cases into scalable technical architectures using enterprise AI platforms, foundation models, agents, retrieval-augmented generation, APIs, data platforms, workflows, and related capabilities.
• Technical feasibility and design – Assess feasibility, complexity, dependencies, implementation options, integration approaches, and production readiness across AI solutions and enterprise systems.
• Hands-on prototyping – Develop proofs of concept, configure AI technologies, support prompt engineering and agent design, and validate approaches with direct technical execution.
• Technology evaluation – Maintain knowledge of enterprise AI platforms and emerging technologies, evaluate third-party products against business needs, and provide clear recommendations for appropriate use cases.
• Reusable patterns and standards – Establish reference architectures, implementation patterns, reusable components, technical guidance, documentation, and lessons learned to promote consistency and reduce duplication.
• Technical delivery leadership – Provide technical direction to internal IT resources, developers, vendors, and implementation partners; review solution designs; and ensure delivery aligns with architecture and requirements.
• Data, security, and governance – Assess data availability, quality, accessibility, and readiness while ensuring designs address security, privacy, Responsible AI, risk mitigation, scalability, maintainability, performance, and supportability.

You’ll win us over by having the following qualifications:

Basic Qualifications:

• Bachelor’s degree required.
• 5+ years of experience with the following:
  • AI, generative AI, modern AI development approaches, software engineering, solution architecture, cloud platforms, data engineering, or related technical disciplines.
  • Large language models, retrieval-augmented generation, agents, APIs, prompt engineering, enterprise integration, and workflow technologies.
  • Designing enterprise technology solutions with cloud, data, identity, security, and application architecture considerations.
  • Rapid prototyping, proofs of concept, technical feasibility assessment, and evaluation of emerging technologies.
  • Communicating complex technical topics to non-technical stakeholders and directing technical work performed by internal or external teams.
• Legally authorized to work in the United States on a continual and permanent basis without company sponsorship.

Preferred Qualifications:

• Experience supporting AI solutions from prototype through scalable production implementation.
• Experience partnering with enterprise architecture, cybersecurity, data, platform, IT, and business teams.
• Experience establishing reusable AI reference architectures, implementation standards, reusable components, and technical documentation.
• Experience evaluating enterprise AI platforms, emerging technologies, and third-party tools while promoting appropriate reuse.
• Experience supporting technical risk assessment, data readiness, Responsible AI practices, security, privacy, and governance requirements.