AI Solution Architect

Summary

Motorola Mobility, a Lenovo company, is hiring an AI Solution Architect to design and validate cost-effective AI solutions that meet client and business requirements. The role focuses on developing end-to-end architectures, guiding development and delivery teams, creating prototypes, and supporting sales and offering teams with technical expertise.

Responsibilities

• Design end-to-end AI solution architectures — data pipelines, model serving, integration, and security controls — against functional and non-functional requirements
• Define deployment strategy and the path from pilot to production; embed responsible-AI, privacy, and regulatory requirements in the design
• Work day-to-day with the development team through build — design reviews, backlog shaping, and unblocking technical decisions
• Build FDE-style mock-ups, prototypes, and thin vertical slices to de-risk a design or make a concept tangible for the client
• Prove out integration patterns, model behavior, and performance assumptions in code before the team commits to them
• Contribute to the codebase where the situation calls for it — spikes, reference implementations, accelerators — without becoming a permanent delivery resource
• Provide and review technical content for RFP/RFI responses, proposals, and SOWs; support pricing with defensible effort and run-cost inputs
• Design and run demos, proofs of concept, and pilots with explicit success criteria and a defined route to production
• Convert repeatable client patterns into productized offerings, accelerators, and reference architectures

Skills

• Bachelor's degree in computer science, engineering, or a related field, or equivalent practical experience
• 2–5 years of combined solution architecture and software development experience, including at least one design taken through to delivery
• Solid understanding of enterprise business applications — ERP, CRM, ITSM, data platforms — and the integration patterns that connect them
• Practical experience delivering AI/ML or generative-AI solutions into production, with working knowledge of a major cloud platform (AWS, Azure, or Google Cloud)
• Command of modern AI patterns: RAG, agentic workflows, fine-tuning, evaluation, and guardrails
• Current hands-on ability to produce a credible prototype independently (Python plus a modern AI/agent framework)
• Strong communication — able to explain a design to engineers, client stakeholders, and sales; willing to travel to client sites as needed
• Cloud or AI professional-level certifications (AWS/Azure/GCP architect or ML specialty)
• Consulting, systems integrator, or ISV background supporting sales and offering teams from a delivery-side role
• Industry or enterprise functional depth (financial services, healthcare, manufacturing; finance, supply chain, service)
• MLOps/LLMOps tooling experience, or forward-deployed and embedded engineering work inside a client's environment

Qualifications

Must Haves

• Bachelor's degree in computer science, engineering, or a related field, or equivalent practical experience
• 2–5 years of combined solution architecture and software development experience, including at least one design taken through to delivery

Nice to Haves

• Solid understanding of enterprise business applications — ERP, CRM, ITSM, data platforms — and the integration patterns that connect them
• Practical experience delivering AI/ML or generative-AI solutions into production, with working knowledge of a major cloud platform (AWS, Azure, or Google Cloud)
• Command of modern AI patterns: RAG, agentic workflows, fine-tuning, evaluation, and guardrails
• Current hands-on ability to produce a credible prototype independently (Python plus a modern AI/agent framework)
• Strong communication — able to explain a design to engineers, client stakeholders, and sales; willing to travel to client sites as needed
• Cloud or AI professional-level certifications (AWS/Azure/GCP architect or ML specialty)
• Consulting, systems integrator, or ISV background supporting sales and offering teams from a delivery-side role
• Industry or enterprise functional depth (financial services, healthcare, manufacturing; finance, supply chain, service)
• MLOps/LLMOps tooling experience, or forward-deployed and embedded engineering work inside a client's environment