Senior Site Reliability Engineer

Overview

Hard Rock Digital is a team focused on becoming the best online sportsbook, casino, and social gaming company in the world. We care about each customer's interaction, experience, behaviour, and insight and strive to ensure we’re always acting authentically.

Rooted in the kindred spirits of the Seminole Tribe of Florida, the new Hard Rock Digital taps a brand known all over the world as the leader in gaming, entertainment, and hospitality. We’re taking that foundation of success and bringing it to the digital space.

What’s the position?

We are looking for a Senior Site Reliability Engineer who combines deep infrastructure expertise with a forward-thinking approach to AI-driven operations. In this role you will maintain and improve the reliability, scalability, and performance of our Java-based applications while pioneering the use of large language models (LLMs), agentic workflows, and intelligent automation to transform how we monitor, respond to, and prevent incidents.

You will design and build autonomous and semi-autonomous AI agents that consume observability data, triage alerts, generate runbooks, automate incident response steps, and surface actionable insights—reducing toil and accelerating mean time to resolution. This is a hands-on engineering role for someone who is equally comfortable tuning a JVM, writing PromQL, and prototyping an agentic pipeline with tool-calling LLMs.

Key Responsibilities

• Application Reliability & Performance — Ensure the availability, reliability, and performance of high-traffic Java-based applications in a distributed environment. Troubleshoot and resolve complex issues across production and non-production environments. Participate in pre- and post-deployment performance testing and monitoring to continuously improve application performance. Optimize Java application performance with a focus on JVM tuning, efficient resource utilization, and horizontal scaling.
• Monitoring, Observability & AIOps — Deploy and manage the Grafana stack (Grafana, Prometheus, Loki, Mimir, Alloy) to deliver real-time monitoring, logging, and alerting. Implement and refine observability strategies that enhance visibility into application and infrastructure health. Create and maintain dashboards, alerts, and log queries for comprehensive system health monitoring. Integrate AI/ML models into the observability pipeline for anomaly detection, predictive alerting, and intelligent alert correlation and noise reduction.
• AI & Agentic Workflow Engineering — Design, build, and operate agentic AI workflows that automate operational tasks such as alert triage, root cause analysis, runbook execution, and incident summarization. Develop tool-calling LLM agents that interact with infrastructure APIs (Kubernetes, Grafana, Jira, Slack, PagerDuty) to execute diagnostic and remediation actions autonomously or with human-in-the-loop approval. Build and maintain MCP (Model Context Protocol) servers and integrations that expose internal systems as tool surfaces for AI agents. Evaluate, select, and operationalize LLM frameworks and orchestration platforms (e.g., LangChain, LangGraph, CrewAI, n8n, or custom solutions) for production-grade agentic systems. Implement guardrails, evaluation harnesses, and feedback loops to ensure AI agent outputs are accurate, safe, and continuously improving. Champion the adoption of AI-assisted development and operations practices across the SRE and broader engineering organization.
• Incident Management & Root Cause Analysis — Support the operations team’s incident response efforts, conduct post-mortems, and identify root causes to prevent recurrence. Leverage AI tools to accelerate incident timelines, auto-generate post-mortem drafts, and surface patterns across historical incidents. Document and share lessons learned, contributing to a culture of continuous improvement.
• Automation & Toil Reduction — Identify repetitive operational tasks and drive automation to reduce toil and improve efficiency.