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# Sr AI Solutions Specialist - Architecture Foundry

- **Company:** CME Group
- **Location:** Chicago, IL, United States
- **Experience:** Expert
- **Salary:** $145,700.0 - $242,900.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, C++ (Programming Language), Cloud Computing, Programming Tools, Python (Programming Language), Machine Learning, Message Broker, Natural Language Processing, Node.Js, Open Source Technology, OpenAI, Tensorflow, Search Technologies, Software Engineering, Pinecone, Google Cloud, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Google Vertex AI, Software Application Programming, Generative AI, Agentic-AI, Pgvector, AI Platforms, Information Technology, CrewAI, AutoGen, Api Design, Google Gemini, Restful APIs, Software Library, Microservices
- **Published:** October 11, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=626e5dce758e5ab8

## About the Role

* 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 LangGraph, AutoGen, 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., DeepLearning.AI, TensorFlow Developer) focusing on natural language processing, LLM application development, or agentic architectures.

## Description

* 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., * Democratize AI Best Practices: Package your code into clear, well-documented starter kits and boilerplates, making it incredibly simple for delivery teams to integrate advanced AI without reinventing the wheel.
 * Co-Create 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.