Senior Site Reliability Engineer

Xplor • Remote, OR

3d • $150,000 - $200,000 • Remote

About The Position

Xplor Technologies is hiring a hands-on Site Reliability Engineer for its AI Engineering team, which builds a shared agent platform supporting Xplor's AI capabilities. The role owns the production reliability of that platform, addressing challenges such as long streaming responses, background agents, model-provider quotas, silent output failures, agents taking actions, and costs that grow with usage. The engineer will establish foundational practices and deployment paths used by product teams, and help define safe operating limits for AI agents working on infrastructure.

The position owns AWS infrastructure and infrastructure-as-code delivery; defines reliability objectives and customer-relevant quality signals; builds observability for conventional and AI-specific metrics; and makes data stores and model-provider integrations resilient. It also establishes multi-tenant protections, operational controls for AI risks, safe guardrails and auditing for infrastructure agents, regional data-handling practices, incident response, on-call participation, game days, safe deployments, and cloud and model cost reduction. The role works with Security, Privacy, and product teams.

This is a fully remote role open to candidates located in the U.S., excluding Hawaii and Alaska. Eastern or Central time zone candidates are preferred. Annual base salary is $150,000 to $200,000 USD.

Requirements

• At least 7 years of experience in SRE, DevOps, or platform engineering, running multi-tenant SaaS in production.
• Strong AWS experience and experience with Terraform at scale; Spacelift, Terraform Cloud, Atlantis, or similar tools.
• Experience building SLO-based alerting and observability with Datadog or similar tools; OpenTelemetry is preferred.
• Track record of leading incidents calmly and turning post-incident reviews into lasting fixes.
• Hands-on PostgreSQL operations experience, including 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.

Nice To Haves

• Experience running LLM or AI workloads in production, or a strong interest in learning how they fail differently from traditional services.
• Experience using AI agents to automate operational work such as incident triage, runbooks, or infrastructure-code review, and a clear understanding of where human review matters.
• Experience in regulated environments such as SOC 2 or PCI DSS.
• Experience with Amazon Bedrock quotas and inference profiles, policy as code, LLM evaluation, AI security tooling, or multi-region data-residency requirements.

Responsibilities

• Define SLOs and error budgets for agent APIs, streaming, and background work, including task success, tool-call failures, and evaluation regressions; alert on customer-relevant signals.
• Own AWS platform infrastructure, including ECS on Fargate, Aurora PostgreSQL, Redis, and Amazon Bedrock, delivered as Terraform through Spacelift.
• Build observability using Datadog, Coralogix, and OpenTelemetry, including token usage, provider latency, and cost per task.
• Make data stores resilient through tested backups and restores, failover, defined RTO/RPO disaster recovery targets, and safe migrations.
• Build model-layer resilience through 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 to control runaway agents and spending.
• Partner with Security on operational controls for AI risks, including prompt injection, excessive agent permissions, and unbounded consumption, with reference to the OWASP Top 10 for LLM Applications.
• Define guardrails for AI agents operating on infrastructure, including permitted changes, actions requiring human approval, and auditing of every action.
• Work with Security and Privacy to keep model inference and customer data in appropriate regions for customers in Australia, New Zealand, the U.S., the UK, and the EU.
• Lead incident response and blameless reviews, participate in on-call, and run game days for important failure scenarios.
• Make deployments safe by default and provide product teams with standard ways to run agent workloads.
• Track and reduce cloud and model spending.

Benefits

• Paid parental leave benefit programs.
• Three extra days off to volunteer and give back to the local community through #GiveBackDays/Commitment to social impact.
• Diversity and inclusion initiatives, including a D&I Council and Global Mentorship Program.
• Access to free mental health support.
• Flexible working arrangements.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed