Chaos Engineer

Enterprise AI is moving from pilots to production, and the constraint is no longer the model — it's the data. Agents are only as good as what they can sense, trust, and act on in the moment, and real‑time, event‑driven data is becoming the foundation every serious AI system runs on.

Solace is the leading platform for the enterprise AI era. Established enterprises worldwide — including RBC Capital Markets, Bosch, Heineken, PSA Singapore, United Airlines, Schwarz Group, and hundreds more — have built their business around Solace to enable intelligent, real‑time experiences, modernize their application and integration landscape, and create seamless digital journeys for their customers, partners, and employees.

So, the next time you drive a car, order furniture online, fly in a plane, check your bank balance on your phone, your positive experience could be a direct result of our technology—and your hard work!

At Solace, reliability is a feature we build deliberately — and you’ll be a key part of how we do it. As an Intermediate Chaos Engineer on our R&D team, you’ll design and run experiments that intentionally stress our platform to expose weaknesses before our customers ever feel them. You’ll bring deep technical expertise in chaos tooling (Chaos Mesh, AWS Fault Injection Simulator, and beyond), sharp debugging instincts, and a genuine drive to uncover hard problems.

You embrace AI as a force multiplier in your work and actively champion an AI‑native approach within the team — automating, accelerating, and enhancing everything from experiment design to root cause analysis. You’ll collaborate closely with other squads to dig into systemic issues, turning findings into actionable improvements that make Solace more resilient for the enterprises that depend on it.

• Design, implement, and execute chaos experiments using Chaos Mesh, AWS Fault Injection Simulator (FIS), and other cloud‑provider fault injection services to proactively surface reliability gaps in the Solace platform
• Leverage AI tools and techniques to enhance experiment design, automate analysis, and accelerate root cause investigations — actively pushing the team toward AI‑native ways of working
• Debug complex, multi‑system failures with rigor and curiosity, tracing issues to their root cause across cloud infrastructure, Kubernetes, and application layers
• Collaborate with engineering squads across R&D to share findings, drive remediation, and embed resilience thinking into how we build and operate
• Define and track reliability metrics and experiment outcomes, translating results into concrete recommendations that reduce customer‑impacting incidents
• Continuously expand the chaos experiment library, staying current with emerging tooling, cloud‑native failure patterns, and industry best practices
• Contribute to a culture of proactive quality — finding issues before our customers can

• 3+ years of hands‑on experience in chaos engineering, site reliability engineering, or a closely related discipline
• Proven experience running chaos experiments with Chaos Mesh and/or cloud‑provider fault injection services such as AWS Fault Injection Simulator, Azure Chaos Studio, or GCP Fault Injection Testing
• Exceptional debugging and root‑cause analysis skills — you love getting to the bottom of a problem and don’t stop until you understand why
• Solid understanding of cloud environments (AWS, Azure, or GCP) and containerized infrastructure, with meaningful Kubernetes experience
• Enthusiasm for AI as a native part of how you work — you actively use AI tools to move faster, think deeper, and do more, and you help bring that mindset to those around you
• Experience working cross‑functionally with multiple engineering teams, comfortable navigating ambiguity and aligning stakeholders around reliability findings
• Strong scripting and automation skills (Python, Bash, or similar) to build and maintain experiment tooling
• Familiarity with observability tooling (metrics, logs, traces) and using them to validate experiment outcomes and system health
• Knowledge of distributed systems concepts and failure modes relevant to high‑throughput,…