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# Site Reliability Engineer

- **Company:** Autonomous Recruiter LLC
- **Location:** Chicago, IL, United States
- **Contract:** Permanent contract
- **Skills:** Algorithmic Trading, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Cloud Computing, Configuration Management, Profiling, Databases, Continuous Integration, Data Centers, Data Stores, Software Debugging, Linux, DevOps, Python (Programming Language), Performance Tuning, RabbitMQ, Reliability Engineering, Ansible, Prometheus, Software Engineering, TCP/IP, Data Logging, Grafana, Containerization, Git Flow, Kubernetes, Low Latency, Deployment Automation, Cassandra, Bare Metal, Real Time Data, Apache Kafka, Vertica, Terraform, OpenSearch, Docker
- **Published:** October 23, 2026
- **Apply:** https://www.careerboard.com/us/en/find-jobs-in-United-States/-BC6EFEEC006951A5F9/

## About the Role

* Strong software engineering skills, ideally in Python and/or Go.
 * Deep Linux expertise, including systems troubleshooting, performance analysis and production operations.
 * Experience building automation, internal tooling or infrastructure platforms in a production environment.
 * Solid understanding of CI/CD, Git-based workflows and modern deployment practices.
 * Experience with Infrastructure as Code, such as Terraform, Ansible or equivalent.
 * Strong observability experience using tools such as Prometheus, Grafana, ELK/OpenSearch or similar.
 * Experience supporting distributed, high-availability or performance-sensitive systems.
 * Strong networking fundamentals, including TCP/IP, latency, throughput and troubleshooting.
 * Ability to debug issues across the full stack: application, operating system, network, storage and infrastructure.
 * Excellent communication skills and a collaborative, ownership-driven mindset., * Experience in HFT, proprietary trading, market making, fintech, gaming, telecoms or another low-latency environment.
 * Exposure to trading systems, market data, exchange connectivity, colocation or Real Time data pipelines.
 * Kubernetes, Docker, EKS or on-premises container platforms.
 * Cloud experience with AWS, including EC2, S3, networking and managed services.
 * Experience with Kafka, RabbitMQ or other streaming/message-queue technologies.
 * Familiarity with C+, Rust or low-level performance profiling.
 * Experience with time-series databases, ClickHouse, Cassandra or other high-performance data stores.
 * Experience with configuration management, bare-metal provisioning or data-centre automation.

## Description

You will work across software engineering, infrastructure and production operations to improve the reliability, performance and scalability of a global trading platform. This is a development-first reliability role: you will write production-quality automation and tooling, improve observability, streamline deployments and help eliminate classes of operational failure.

You will collaborate closely with trading-system engineers, quant researchers, infrastructure engineers and platform teams in a fast-moving, low-latency environment.

What You'll Do

 * Design, build and maintain automation, deployment and configuration tooling for trading and research infrastructure.
 * Develop internal platforms and services using Python and/or Go.
 * Build and improve CI/CD pipelines, release workflows, environment management and deployment automation.
 * Operate and enhance Linux-based production environments across on-premises data centres and cloud infrastructure.
 * Build observability solutions including metrics, logging, tracing, alerting, dashboards and automated remediation.
 * Investigate and resolve complex production incidents, performing deep technical root-cause analysis.
 * Improve system resilience through redundancy, failover design, capacity planning and proactive performance tuning.
 * Use Infrastructure as Code to provision and manage infrastructure, including Terraform and/or Ansible.
 * Support containerised workloads and Kubernetes-based platforms where appropriate.
 * Work with networking, storage and compute teams to optimise low-latency, high-throughput systems.
 * Participate in an on-call rotation and drive improvements through post-incident reviews and automation.
 * Partner with engineering teams to establish best practices for reliability, testing, deployment and operational excellence.