• Lead enterprise AI adoption by partnering with business units, engineering teams, product leaders, architecture, security, legal, compliance, and procurement to identify, evaluate, and implement practical AI solutions.
• Partner with business units to translate business problems into AI-enabled solution designs, including agentic workflows, LLM applications, retrieval-augmented generation, automation, decision support, and AI-assisted engineering capabilities.
• Evaluate AI vendors, platforms, foundation models, agentic tools, orchestration frameworks, and emerging AI capabilities through hands-on testing, proof of concepts, benchmarks, and technical assessments.
• Establish decision matrices and evaluation criteria to help teams select the right AI tools, models, platforms, and vendors based on use case fit, cost, performance, security, scalability, integration complexity, reliability, compliance, and Responsible AI considerations.
• Design and build reusable agentic frameworks, reference architectures, design patterns, prompts, orchestration approaches, and integration models that business units can adopt and extend.
• Develop prototypes, proof of concepts, and technical accelerators that demonstrate new AI ideas, validate business value, reduce uncertainty, and create a path from experimentation to production.
• Partner with business units to translate business problems into AI-enabled solution designs, including agentic workflows, LLM applications, retrieval-augmented generation, automation, decision support, and AI-assisted engineering capabilities.
• Define enterprise-wide AI adoption patterns, including vendor integration standards, API patterns, model selection guidance, data access approaches, observability, guardrails, evaluation practices, and deployment models.
• Provide hands-on engineering leadership in building and testing AI solutions across cloud platforms, enterprise systems, APIs, microservices, event-driven architectures, and modern DevOps environments.
• Stay current on the AI vendor landscape, emerging model capabilities, agentic frameworks, industry trends, and enterprise AI patterns, and translate those insights into actionable recommendations.
• Influence senior technology and business leaders by clearly communicating tradeoffs, risks, implementation options, and strategic recommendations for AI investments.