Distinguished Engineer - AI Adoption
Listed on 2026-07-08
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Software Development
AI Engineer (Applied/Software), Software Architect
Distinguished Engineer – AI Adoption
Location:
Chicago, IL.
Salary range: $254,584 to $381,876.
Hybrid/Virtual work policy:
Candidates must be within reasonable commuting distance unless accommodation is granted as required by law.
- Partner with business units, engineering teams, product leaders, architecture, security, legal, compliance, and procurement to identify, evaluate, and implement practical AI solutions.
- 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 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.
Requires a bachelor’s degree in Information Technology, Computer Science, Engineering, or a related field and a minimum of 15 years of experience in software engineering, including leading, designing, implementing, and operating distributed systems and large‑scale architectures, or equivalent education and experience. Experience designing, developing, and maintaining software using Python, Java, or comparable object‑oriented programming languages is required, as is experience designing and implementing APIs, microservices, event‑driven architectures, and applying cloud, CI/CD, and Dev Ops practices.
PreferredSkills, Capabilities and Experiences
- 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.
- Strong understanding of AI adoption strategies, including use case discovery, proof‑of‑concept development, vendor assessment, solution selection, technical enablement, and production‑readiness planning.
- 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.
- Ability to create decision matrices, technical scorecards, benchmark approaches, and recommendation frameworks for selecting AI tools, models, platforms, and vendors.
- Proven ability to rapidly prototype new ideas, test emerging technologies, validate feasibility, and convert successful experiments into scalable engineering patterns.
- Experience mentoring engineering teams and creating enablement materials, reference implementations, reusable frameworks, and adoption playbooks for enterprise AI.
- Demonstrated ability to influence executives, engineering leaders, architects,…
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