AI-DNA SVP of Site Reliability Engineering

By the time a human engineer reaches the incident, the AI agents on the team have already validated hypotheses against years of prior incidents and RCAs, parsed the logs and code paths, identified the failure pattern, and proposed — or applied — a remediation inside policy guardrails. That is the operating model you will lead: agentic SRE at Khoros — one of IgniteTech's flagship enterprise SaaS platforms, powering customer communities and social engagement for the world's largest brands — where reliability translates directly into retention, revenue, and customer trust. What You Will Be Doing Owning platform reliability and customer‑experience outcomes for an AI‑native community and social engagement platform — uptime trending up, MTTR trending down week over week, customer satisfaction trending up. As the owner of the outcome, you will be the senior leader involved in critical situations (CritSits) with customers and the face of those incidents and escalations. Designing, governing, and continuously extending the AI agent system that does the operations work — pre‑triage, alarm authoring, blocking change‑validation gates, permanent‑fix lifecycle chase, customer‑facing RCA drafting, auto‑healing on whitelisted operations. The harness is the product; the team's output is the agent surface. Leading a small, senior, fully remote team of SRE / SaaS / DevOps engineers across multiple time zones — no L1/L2 tier; every engineer ships agents, writes runbooks, and owns the incident loop end to end. Staying hands‑on yourself — driving outage bridges when the blast radius warrants it, writing RCAs, shipping agent code. A substantial share of your week goes to personal agent build and maintenance. Representing operations directly to enterprise customer leadership and to the CEO — translating reliability investment into retention, revenue, and customer‑experience outcomes. What You Will NOT Be Doing Growing the team to solve problems that AI should be solving. If you find yourself adding headcount to compensate for agent gaps, you are off‑strategy. Fix the AI, not the org chart. Running a deck‑and‑roadmap executive function. You will be at the keyboard, on the bridge, and in the agent code regularly. If you have not personally written or shipped production code in the last 12 months, the role will be uncomfortable. Owning product engineering or customer support. Your scope is the operating layer — where reliability and the AI agent surface live. Drowning in tickets. The point of this role is to remove ticket‑throughput as the primary operating metric, not optimize it. Sitting on every bridge call. You will drive the bridge personally when a top‑tier customer is down or the blast radius is material. The rest of the time, the operator tier handles incident execution and the AI surface absorbs the routine. Treating this role as a credential or a résumé line. The bar is ownership and obsession, not titles. Responsibilities Deliver the reliability outcomes — and own them like a founder accountable for them. Platform uptime trending up, MTTR trending down week over week, customer satisfaction trending up. When something is down, that is your problem; you do not sleep peacefully through a customer outage. Own enterprise‑customer escalations. Be the executive face of operations when a customer at the largest tier needs one, and engineer the operating system so escalations keep falling. Customer trust is the metric — measured in retention and contract expansion. Set and enforce AI agent quality and governance. Every agent in production has a defined scope, a measured acceptance rate, an escape hatch, and a guardrail anchored to documented failure modes. You hold the bar — including against agents that propose to skip a step. Recruit, develop, and retain a top‑1% senior‑only team. No L1, no L2 — every engineer is a pioneer in their craft. Recruiting looks more like courtship than triage. Shape the operating model and the playbook. Where AI does more, where humans must stay, what the agent surface looks like 6 and 12 months from now. The industry playbook for agentic SRE is being written right now — you write a meaningful part of it. Partner with product engineering and customer success peers. The operating layer is the hinge between them; reliability outcomes depend on those interfaces working. Own platform availability end‑to‑end: you will be expected to read, interpret, and act on operational signals, including incident histories, status dashboards, and SLA performance data. Knowing the current health posture of every system under your ownership, at any moment, is baseline. Requirements Extreme ownership. You run operations as if it were your own business — your money, your reputation, your customers. Expect to bring that same intensity here. 10+ years operating SaaS at meaningful scale, with at least 3 years in an SVP, VP, or Head‑of role managing a senior‑only engineering organization. AI‑First DNA — already operating this way today, not open to AI. You have built or led AIOps / agentic incident response / auto‑remediation systems in production. You use Claude Code, Cursor, or equivalent agentic coding tools daily. Deep AWS production experience at scale — real multi‑AZ, multi‑account production estates. Certifications are nice; production scars are mandatory. Hands‑on senior engineer who leads, not a deck‑and‑roadmap executive. You drive outage bridges, write RCAs, ship code, and evaluate agent designs. Track record of delivering reliability and customer‑experience improvements week over week while holding or shrinking headcount. Fluent / advanced English — you interface directly with the CEO and with enterprise customer leadership. Time‑zone overlap with US morning hours (roughly 13:00–17:00 UTC). OFAC‑clear country of residence. Nice to Have Pioneer voice in this field — substantive original posts, talks, articles, or open‑source work on agentic SRE / harness‑over‑headcount / AI‑replacing‑ops‑work. A documented history of being obsessed with one hard thing outside of mainstream work — a side project, an open‑source contribution, an unusual hobby pursued with depth. Background in multi‑tenant B2B SaaS at scale — community, social, customer‑experience, observability, AIOps, or developer‑tooling platforms. Hands‑on familiarity with modern observability and incident‑response stacks (Grafana, Loki, Prometheus, OpsGenie, PagerDuty, Datadog, or equivalents). What You Will Learn You will design and run one of the first operating functions built AI‑native from the inside out at enterprise scale. You will define what works, publish what doesn't, and shape a playbook the rest of the industry is still circling. The team that gets this right in the next 12 months becomes the reference everyone else cites — you will lead that team. Working Conditions Enterprise scale, startup cadence. The customer base, the platform footprint, and the contractual stakes are those of a large enterprise SaaS. The operating model is the opposite: outcomes are measured week over week, not quarter over quarter; decisions are made in days, not committees; the playbook is rewritten as the field evolves. Fully remote, async‑first, global team. Hire from anywhere with US‑morning UTC overlap; no office; work happens where the work is best done. The team is small and senior — no L1/L2 layer beneath you. No token limits. No tooling limits. The harness is the product, and we resource it accordingly. If the right answer is more compute, more inference, a better model, or a tool we have not bought yet, we buy it. #J-18808-Ljbffr IgniteTech