Senior Site Reliability Engineer

Xplor Technologies powers the experiences at the heart of everyday life. Through modern vertical software, embedded payments, and AI-powered capabilities, we help businesses in fitness, recreation, golf and club, field services, laundry, education, and other membership-based and service-based industries simplify operations, uncover insights, and elevate customer and member experiences.  About the opportunity AI Engineering builds the shared agent platform behind Xplor's AI capabilities: the agent runtime, model gateway, retrieval, guardrails, evaluations, and cost controls. We build the hard parts once, so every product team can ship AI features faster and safely. We're looking for a hands-on Site Reliability Engineer to own how our agent platform behaves in production. AI workloads bring new reliability problems: long streaming responses, background agents, model-provider quotas, outputs that can fail silently, agents that take real actions, and spend that grow with usage. You'll treat them as first-class engineering problems. As AI agents take on more of our operational work, you'll decide what they're allowed to do and make sure they do it safely. Why this role is exciting Reliability for a new kind of workload . The playbook for running AI agents in production is still being written, and you'll help write it. Scale with real stakes. A multi-tenant platform serving thousands of businesses, and AI features they depend on to run their day. Greenfield foundations . Define the SLOs, alerting, game days, agent guardrails, and standard deployment paths that every product team will build on. Cost is an engineering problem . Token economics, model routing and cost per task matter as much as uptime. AI-native operations . Use AI agents to automate toil, speed up investigations and improve infrastructure code; with the approval gates, scoped permissions and audit trails that make it safe. What you’ll do Define SLOs and error budgets for agent APIs, streaming and background work, including quality signals like task success, tool-call failures and evaluation regressions, and alert on what matters to customers. Own the platform's AWS infrastructure (ECS on Fargate, Aurora PostgreSQL, Redis and Amazon Bedrock) as Terraform delivered through Spacelift. Build observability in Datadog and Coralogix with OpenTelemetry, including AI signals like token usage, provider latency and cost per task. Make data stores resilient, with tested backups and restores, failover, defined disaster recovery targets (RTO/RPO) and safe migrations. Build resilience into the model layer: provider failover, quota and rate-limit management, and safe rollout and rollback of model and prompt changes. Protect the platform under multi-tenant load with per-tenant quotas, isolation and circuit breakers that stop runaway agents and runaway spend. Partner with Security on operational controls for AI risks, including prompt injection, excessive agent permissions and unbounded consumption (OWASP Top 10 for LLM Applications). Set the guardrails for AI agents that work on our infrastructure: what they can change, which actions need human approval, and how every action is audited. Work with Security and Privacy to keep model inference and customer data in the right regions for customers in Australia, New Zealand, the US, the UK and the EU. Lead incident response and blameless reviews, take part in on-call, and run game days for the failures that matter most. Make deployments safe by default, and give product teams simple, standard ways to run agent workloads. Track and reduce cloud and model spend. Our stack Cloud: AWS: ECS on Fargate, Aurora PostgreSQL with pgvector, ElastiCache Redis, CloudFront, Route 53, Secrets Manager and SSM AI: Amazon Bedrock and other model providers behind a multi-provider gateway Infrastructure as code: Terraform, delivered through Spacelift Observability: Datadog, Coralogix and OpenTelemetry Delivery: GitHub Actions, containers, Amazon ECR and preview environments This position has an annual base salary range of $150,000 to $200,000 USD. This is a fully remote role open to candidates based anywhere in the U.S., excluding Hawaii and Alaska. We’d prefer candidates in the Eastern or Central time zones. #LI-Remote What would make me a good candidate? 7+ years in SRE, DevOps or platform engineering, running multi-tenant SaaS in production Strong AWS experience and Terraform at scale (Spacelift, Terraform Cloud, Atlantis or similar) Experience building SLO-based alerting and observability with Datadog or similar tools, ideally with OpenTelemetry A track record of leading incidents calmly and turning post-incident reviews into lasting fixes Hands-on PostgreSQL operations: backups, point-in-time recovery, performance tuning and upgrades Production-quality coding skills for automation and tooling A least-privilege mindset and a disciplined approach to using AI tools It would be great if you also have Experience running LLM or AI workloads in production, or a real interest in learning how they fail differently from traditional services Use of AI agents to automate operational work, such as incident triage, runbooks or infrastructure-code review, and a clear sense of where human review still matters Experience in regulated environments such as SOC 2 or PCI DSS Amazon Bedrock quotas and inference profiles, policy as code, LLM evaluation, AI security tooling, or multi-region data-residency requirements At Xplor, we believe that the best innovation and ideas happen at the intersection of differences - people of different cultures, generations, disciplines, and lived experiences. So even if you think you don’t quite tick all the boxes, we still encourage you to apply. Values and Life at Xplor Our five core values guide us from how we hire and recognise our team members to how we interact with our customers day to day: Find a better way Do the right thing Say it straight