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About This Role
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 solutions.
In 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 Do
Applied 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 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.
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 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.
What You'll Get
- Become a cornerstone of a high-visibility function where your contributions directly shape the core technology foundations of financial markets, tackling technical challenges at an unmatched scale.
- A supportive environment fostering career progression, continuous learning, and an inclusive culture.
- Broad exposure to CME's diverse products, asset classes, and cross-functional teams.
- A competitive salary and comprehensive benefits package.
Explore our full range of benefits *
#LI - JK1
CME Group is committed to offering a competitive total rewards package for our employees that recognizes their contributions to the business and reflects our long-term investment in their future. The pay range for this role is $145,700-$242,900. Actual salary offered will be dependent on a wide array of factors including but not limited to: relevant experience, skills, education and comparison to internal employees (where relevant). Our compensation program also includes an annual target bonus opportunity for all employees, as well as the opportunity to become an owner in the company through our broad-based equity program. Through our benefits program, we strive to offer flexibility, value and choice. From comprehensive health coverage, to a retirement package that includes both a 401(k) and an active pension plan, to highly competitive education reimbursement provisions, paid time off and a mental health benefit, CME Group offers a holistic benefits package for our team and their dependents. CME Group: Where Futures are Made
CME Group is the world’s leading derivatives marketplace. But who we are goes deeper than that. Here, you can impact markets worldwide. Transform industries. And build a career by shaping tomorrow. We invest in your success and you own it – all while working alongside a team of leading experts who inspire you in ways big and small. Problem solvers, difference makers, trailblazers. Those are our people. And we’re looking for more.
At CME Group, we embrace our employees' unique experiences and skills to ensure that everyone’s perspectives are acknowledged and valued. As an equal-opportunity employer, we consider all potential employees without regard to any protected characteristic.
Important Notice: Recruitment fraud is on the rise, with scammers using misleading promises of job offers and interviews to solicit money and personal information from job seekers. CME Group adheres to established procedures designed to maintain trust, confidence and security throughout our recruitment process.
Salary Context
This $145K-$242K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At CME Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($194K) sits 8% below the category median. Disclosed range: $145K to $242K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
CME Group AI Hiring
CME Group has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $135K - $242K.
Location Context
AI roles in Chicago pay a median of $193,440 across 429 tracked positions. That's 9% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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