Job description

CME Group is looking for a senior and experienced Staff Agentic Software Engineer to join a dynamic team responsible for our mission-critical Real-time Positions & Risk Management Systems. This role is pivotal in driving both the technical evolution of our financial platforms and the transformation of our engineering workflows. You will lead the team in transitioning from traditional software development to an agentic engineering paradigm —leveraging advanced LLM coding tools (e.g., Gemini CLI, Antigravity) to build high-performance systems faster, safer, and at greater scale. As a technical leader, you will balance aggressive AI-driven velocity with the strict safety, low-latency, and zero-downtime requirements of CME Group s core clearing and risk management functions. Principal Responsibilities Lead the architecture, design, and development of high-volume, low-latency Java applications on Google Cloud Platform (GCP) for mission-critical systems, ensuring ultra-high availability, low jitter, and thread safety. Lead the team’s shift toward agentic software engineering. Standardize toolchains, system prompts, context repositories, and agentic loops across the development lifecycle. Build and maintain the shared agent infrastructure: repo-level agent context, MCP server integration, codebase indexing pipelines, and local developer tooling that feed deep, domain-specific context into LLM agents. Design dynamic evaluation harnesses, automated test suites, and CI/CD guardrails specifically tailored to audit, test, and validate AI-generated code for concurrency bugs, memory leaks, and performance regressions. Maintain and enhance high-throughput CI/CD automation pipelines to ensure seamless, secure, and reliable software delivery into production. Mentor engineers through the workflow shift; lead hands-on workshops to train traditional software developers into proficient AI-native engineers. Core Technical Expertise Bachelor’s degree or higher in Computer Science, Mathematics, Financial Engineering, or a related field, with 8 years of hands-on experience building, deploying, and maintaining scalable real-time systems across the full stack. Expert-level proficiency in Java and the Spring framework . Deep, hands-on experience designing and debugging multi-threaded concurrent applications, lock-free data structures, memory management, thread pools, and race condition diagnostics. Hands-on experience with AI coding agents (e.g. Gemini CLI, Claude Code, Codex, etc) in real production workflows: multi-step agent tasks, agent-authored PRs, agent-driven test generation. Deep experience with Google Cloud Platform ( GCP ) services (GKE, Pub/Sub, BigQuery, Cloud Run, Dataflow) and real-time messaging frameworks ( Kafka, MQ, Flink ). Strong proficiency in SQL, Postgres DB, and Python (for scripting, automated evals, data analysis, or tooling integration). Experience with distributed tracing, SLO/SLI monitoring, and chaos engineering in production environments where system failure carries direct financial or regulatory impact. Agentic Engineering Daily operational fluency with CLI and terminal-based agent environments ( Claude Code, Gemini CLI, Codex ) as well as agentic IDEs ( Cursor, Antigravity ). Direct experience configuring system context, building or integrating Model Context Protocol (MCP) servers, function calling, and structured domain-prompting. Proven track record of designing property-based tests, static analysis rules, and code-review workflows specifically built to catch AI hallucinations, edge-case failures, and security vulnerabilities. Demonstrated ability to establish team-wide AI coding norms, measure developer outcome velocity, and champion an AI-first engineering culture. Staff-level Expectations Drive architecture decisions across team boundaries and be able to articulate tradeoffs clearly to both engineers and stakeholders. Be able to operate under pressure and on-call for system where failure has direct financial or regulatory input Elevate team capabilities through rigorous code reviews, design reviews, pairing, and active mentorship. Contribute to development tooling and engineering culture initiatives. Drive technical leadership by mentoring the team on agentic AI capabilities, accelerating feature delivery while maintaining strict code quality and reliability. Desirable Qualifications Experience developing software for financial risk management, high-frequency trading, or clearing systems. Experience building internal developer tools, CLI extensions, or custom LLM evaluation harnesses. Familiarity with local model deployments or fine-tuning workflows for enterprise dev environments. 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 $125,800-$209,600. 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. Learn more here .

Requirements

Must-haves

Principal Responsibilities
Lead the architecture, design, and development of high-volume, low-latency Java applications on Google Cloud Platform (GCP) for mission-critical systems, ensuring ultra-high availability, low jitter, and thread safety.
Lead the team’s shift toward agentic software engineering. Standardize toolchains, system prompts, context repositories, and agentic loops across the development lifecycle.
Build and maintain the shared agent infrastructure: repo-level agent context, MCP server integration, codebase indexing pipelines, and local developer tooling that feed deep, domain-specific context into LLM agents.
Design dynamic evaluation harnesses, automated test suites, and CI/CD guardrails specifically tailored to audit, test, and validate AI-generated code for concurrency bugs, memory leaks, and performance regressions.
Maintain and enhance high-throughput CI/CD automation pipelines to ensure seamless, secure, and reliable software delivery into production.
Mentor engineers through the workflow shift; lead hands-on workshops to train traditional software developers into proficient AI-native engineers.
Core Technical Expertise
Hands-on experience with AI coding agents (e.g. Gemini CLI, Claude Code, Codex, etc) in real production workflows: multi-step agent tasks, agent-authored PRs, agent-driven test generation.
Deep experience with Google Cloud Platform ( GCP ) services (GKE, Pub/Sub, BigQuery, Cloud Run, Dataflow) and real-time messaging frameworks ( Kafka, MQ, Flink ).
Strong proficiency in SQL, Postgres DB, and Python (for scripting, automated evals, data analysis, or tooling integration).
Experience with distributed tracing, SLO/SLI monitoring, and chaos engineering in production environments where system failure carries direct financial or regulatory impact.
Agentic Engineering Daily operational fluency with CLI and terminal-based agent environments ( Claude Code, Gemini CLI, Codex ) as well as agentic IDEs ( Cursor, Antigravity ).
Direct experience configuring system context, building or integrating Model Context Protocol (MCP) servers, function calling, and structured domain-prompting.
Proven track record of designing property-based tests, static analysis rules, and code-review workflows specifically built to catch AI hallucinations, edge-case failures, and security vulnerabilities.
Demonstrated ability to establish team-wide AI coding norms, measure developer outcome velocity, and champion an AI-first engineering culture.
Staff-level Expectations
Drive architecture decisions across team boundaries and be able to articulate tradeoffs clearly to both engineers and stakeholders.
Be able to operate under pressure and on-call for system where failure has direct financial or regulatory input
Elevate team capabilities through rigorous code reviews, design reviews, pairing, and active mentorship.
Contribute to development tooling and engineering culture initiatives.
Drive technical leadership by mentoring the team on agentic AI capabilities, accelerating feature delivery while maintaining strict code quality and reliability.
Desirable Qualifications
Experience developing software for financial risk management, high-frequency trading, or clearing systems.

Nice-to-haves

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Keywords to mirror

llmaijavagcpci/cdsqlpostgrespython

Platform: workday
Effort: long

Benefits

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Vision

401(K)

Paid Time Off