Sr AI Solutions Specialist - Architecture Foundry

Are you a highly versatile developer who is passionate about translating cutting-edge Generative AI research into production-ready software? Do you want to build the smart interfaces, agentic workflows, and automated microservices that will power the future of financial markets?

CME Group is seeking a Sr AI Solutions Specialist to join our revolutionary Architecture Foundry within the Central Hub. Sitting inside our innovative, cross-functional "Architecture-as-a-Product" pod, you will partner directly with Enterprise Architects and Cloud Platform Engineers to turn visionary architectural concepts into living, breathing AI-powered  this role, you will be the premier Force Multiplier for our applied architecture stories and epics.

Rather than building isolated models, you will act as a versatile, multi-disciplinary AI specialist. You will take the robust security patterns, landing zones, and infrastructure blueprints designed by the central hub and use them to engineer reusable application-level templates, multi-agent systems, and production-ready code blocks. By building these "Paved Paths," you will empower delivery teams across CME Group to rapidly adopt and scale secure GenAI capabilities.

What You'll DoApplied AI & Agentic Engineering Build Reusable AI Assets:
Design, build, and maintain enterprise-grade software templates, reusable SDKs, and modular microservices that wrap GenAI capabilities (e.g., prompt management, retrieval-augmented generation [RAG], embeddings) into standard developer tools.

Architect AI Agents:
Design and deploy robust multi-agent systems and agentic architectures capable of planning, reasoning, memory retention, tool usage, and execution across complex enterprise APIs.Translate Architecture into Code:
Partner with Enterprise Architects to implement their strategic design patterns, turning abstract standards into functional codebases that prove the viability of next-generation AI platforms.

Prototype to Production (MVPs):
Rapidly develop functional prototypes and proof-of-concepts (POCs) for high-impact AI use cases, ensuring code is clean, modular, and optimized for production hand-off.

Developer Enablement & Mentorship Democratize AI Best Practices:
Package your code into clear, well-documented starter kits and boiler plates, making it incredibly simple for delivery teams to integrate advanced AI without reinventing the  Paved Paths:
Work with the Pattern Council to certify and publish your templates as standard developer building blocks.

Collaborative Support:
Embed briefly with engineering pods to help them adopt your templates, troubleshoot complex API/LLM integration issues, and scale their implementations.

What You'll Bring Experience & Education Professional Background: 3–5+ years of progressive, hands-on software engineering experience, with a proven track record of writing production-grade, highly reusable, and modular code libraries.

Enterprise Exposure:
Experience working within large, complex corporate environments or financial services institutions, demonstrating familiarity with enterprise security standards, data privacy controls, and API-driven architectures.

Education:

Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related technical discipline.

Technical & AI Expertise Software Engineering Core:
Deep proficiency in Python (the industry standard for AI enablement) alongside experience in enterprise languages (e.g., Java, C++, or Node.js).Generative AI & LLMs:
Hands-on experience developing applications using state-of-the-art Large Language Models (LLMs) from Google (Gemini/Vertex AI), OpenAI, or open-source ecosystems.

Agentic Frameworks & Orchestration:
Proven experience building agentic workflows and multi-agent orchestration systems using frameworks such as Lang Graph, Auto Gen, CrewAI, or Google Vertex AI Agent Builder.

Modern Development Ecosystem:
Strong understanding of semantic search, vector databases (e.g., Pinecone, pgvector), chunking strategies, prompt engineering patterns, and RAG architectures.

API & Integration Patterns:
Advanced skills in building and consuming RESTful/gRPC APIs, microservice design, and asynchronous message broker patterns.

Certifications (Highly Desired)
Google Cloud Certified:
Professional Machine Learning Engineer or Professional Cloud Developer.

Specialized AI Credentials:
Credentials from recognized programs (e.g., Deep Learning.AI, Tensor Flow Developer) focusing on natural language processing, LLM application…