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-basedindustriessimplify 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 Open Telemetry, 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, Elasti Cache Redis, Cloud Front, Route 53, Secrets Manager and SSM
• AI:
  Amazon…