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Mid-sized brand, metro location, and mid-level AI role create moderate applicant competition.
AI architecture and cloud skills are transferable but require domain-specific governance and MLOps experience.
Explicit years, mandatory AI experience, cloud and architecture expertise make screening fairly strict.
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Lead AI initiative implementation from concept through delivery, providing hands-on technical guidance on solution design, architecture trade-offs, integration, and best practices.
Develop and maintain reusable AI reference architectures, enablement materials, and contribute to AI standards, governance, and responsible AI practices across teams.
Create and deliver AI enablement programs including training, workshops, demos and support integration of AI capabilities to improve productivity, product capabilities, and customer experience.
5+ years experience in software engineering, architecture, cloud architecture, or enterprise architecture.
2+ years experience specifically designing, implementing, or enabling AI and/or machine learning solutions.
Strong knowledge of Generative AI, Large Language Models, AI platforms (Azure AI Services, OpenAI, AWS Bedrock, Google Vertex AI, etc.), AI security, privacy, compliance, and responsible AI principles.
Experience developing enterprise standards, governance frameworks, and AI operational practices including DevOps and MLOps.
Experienced in leading AI transformation or enablement initiatives at scale within large organizations.
Skilled in creating enterprise AI Centers of Excellence and strong at stakeholder management and organizational influence without direct authority.
Familiar with enterprise SaaS products and cloud platforms (Azure, AWS), with certifications in AI engineering or architecture preferred (e.g., Azure AI Engineer Associate, AZ-305).