Distinguished Engineer - AI Adoption

Elevance Health•Chicago, IL

1d•$254,584 - $381,876•Hybrid

About The Position

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.

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.
Strong programming in Python, Java, or similar required.
Experience with APIs, microservices, and event-driven architectures, cloud platforms, and CI/CD and DevOps practices required.

Nice To Haves

Experience evaluating and implementing AI solutions from multiple vendors, including foundation model providers, cloud AI platforms, AI coding tools, agentic platforms, orchestration frameworks, and enterprise AI products is preferred.
Strong understanding of AI adoption strategies, including use case discovery, proof-of-concept development, vendor assessment, solution selection, technical enablement, and production-readiness planning is preferred.
Experience developing agentic AI frameworks, reusable design patterns, prompt strategies, orchestration flows, tool-calling patterns, RAG patterns, workflow automation, and human-in-the-loop models is preferred.
Ability to create decision matrices, technical scorecards, benchmark approaches, and recommendation frameworks for selecting AI tools, models, platforms, and vendors is preferred.
Proven ability to rapidly prototype new ideas, test emerging technologies, validate feasibility, and convert successful experiments into scalable engineering patterns is preferred.
Experience mentoring engineering teams and creating enablement materials, reference implementations, reusable frameworks, and adoption playbooks for enterprise AI is preferred.
Demonstrated ability to influence executives, engineering leaders, architects, vendors, and cross-functional stakeholders through technical credibility, clear communication, and hands-on delivery is preferred.
Experience including hands-on technical leadership in designing, evaluating, prototyping, or delivering enterprise-scale AI, cloud, platform, integration, or distributed systems solutions is preferred.

Responsibilities

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.
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.