Distinguished Engineer - AI Adoption

Distinguished Engineer - AI Adoption

Distinguished Engineer - AI Adoption

Location:

This role requires associates to be in-office 3 days per week, fostering collaboration and connectivity, while providing flexibility to support productivity and work-life balance. This approach combines structured office engagement with the autonomy of virtual work, promoting a dynamic and adaptable workplace. Alternate locations may be considered if candidates reside within a commuting distance from an office.

The Distinguished Engineer – AI Adoption will be responsible for accelerating enterprise adoption of artificial intelligence by evaluating emerging AI technologies, designing reusable frameworks, developing prototypes, and enabling business units to safely and effectively apply AI to high-value use cases. This role will serve as a hands-on technical leader across enterprise AI initiatives, working with internal teams and external AI vendors to assess capabilities, test solutions, define decision frameworks, and establish repeatable engineering patterns.

The Distinguished AI Adoption Engineer will help business units identify, prototype, and scale AI solutions using the right combination of models, platforms, tools, and architectures.

How you will make an impact:

• 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 Dev Ops 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.

Minimum Requirements:

• Requires a Bachelor's degree in Computer Science, Information Technology, or related field and a minimum of 15 years of experience in software engineering, distributed systems and large-scale architecture, or any combination of education and experience, which would provide an equivalent background. Experience delivering production-grade AI/ML systems at scale, building agentic systems or complex LLM-based applications, modernizing large, complex legacy systems, and delivering high-quality systems also required.
  
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