Staff Agentic Software Engineer

CME Group•Chicago, IL

2d•$125,800 - $209,600•Hybrid

About The Position

CME Group is seeking a senior and experienced Staff Agentic Software Engineer to join a team responsible for mission-critical Real-time Positions & Risk Management Systems. This role is key to advancing the technical capabilities of financial platforms and transforming engineering workflows. The engineer will lead the transition from traditional software development to an agentic engineering paradigm, utilizing advanced LLM coding tools to build high-performance systems efficiently and safely, meeting the strict requirements of CME Group's core clearing and risk management functions.

Requirements

Bachelor’s degree or higher in Computer Science, Mathematics, Financial Engineering, or a related field.
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.
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.

Nice To Haves

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.

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, standardizing toolchains, system prompts, context repositories, and agentic loops across the development lifecycle.
Build and maintain the shared agent infrastructure, including repo-level agent context, MCP server integration, codebase indexing pipelines, and local developer tooling.
Design dynamic evaluation harnesses, automated test suites, and CI/CD guardrails 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 for seamless, secure, and reliable software delivery.
Mentor engineers through the workflow shift and lead workshops to train traditional software developers into proficient AI-native engineers.
Drive architecture decisions across team boundaries and articulate tradeoffs clearly.
Operate under pressure and on-call for systems 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.