About the role
Introduction to Demandbase: Demandbase is the only pipeline AI platform that empowers GTM teams to automate growth at scale. With a unified view of data, insights, actions, and outcomes, B2B enterprises can seamlessly align and execute their account-based GTM strategies with confidence. Thousands of businesses trust Demandbase to maximize revenue, minimize waste, and consolidate their data and tech stacks – all in one platform. As a company, we’re as committed to growing careers as we are to building world-class technology. We invest heavily in people, our culture, and the community around us. We have also continuously been recognized as One of The Best Places To Work in the San Francisco Bay Area by Fortune, and One of The 60 Best Companies To Sell For by Selling Power. Our offices are located in San Francisco, New York, Austin, Seattle, India, and the United Kingdom.
About
the Role AI Runtime Services owns the shared runtime that AI at Demandbase runs on — two ways. First, it's the layer that enables and supports the AI features in the Demandbase platform: product teams ship LLM features on top of it without reinventing model access, spend control, safety, and observability. Second,
the team builds and supports internal AI solutions — the tooling and services the company uses to work with LLMs day to day. The pillars underneath both: the LLM gateway, cost controls, observability, evals infra, caching, and guardrails. The surface is broad and
the team is early. You won't be handed a narrow slice: expect to move across the gateway one week and the ingest pipeline the next — and across product-facing runtime and internal tooling — and to own what you build in production.
What you'll own The LLM gateway. The single front door to every model provider we use — keys, routing, rate limits, failover, provider quotas. When it's down, every AI feature at Demandbase is down. Reliability, for real. SLOs, on-call, incident response, and the postmortems for the runtime. This is a genuine SRE ownership role, not "build it and let someone else run it." Cost control. Per-team and per-model attribution, budgets, quotas. LLM spend is the kind of line item that quietly triples; your job is to make it legible and then make it smaller. LLM observability. Traces, spans, prompt/response capture, ingest and billing visibility for every AI feature in the company. Evals and experimentation infra. The shared harnesses, datasets, and scoring plumbing product teams use to know whether a prompt or model change actually made things better. Caching and guardrails. Response and semantic caching to cut latency and redundant spend; input/output safety, PII handling, and policy enforcement at the gateway. What we need Strong production infrastructure background: Kubernetes, AWS, Terraform, GitOps. You've operated systems that people were paged for, and you've been the one paged. Python, at a level where you're comfortable owning services and tooling in it — not just scripting. Real on-call and incident-response experience. You can talk about an incident you ran, what you got wrong, and what you changed afterward. Observability fluency beyond "we have dashboards": you've instrumented systems, chased cardinality and cost in a metrics/logging backend, and built alerts that fire when they should. Hands-on familiarity with how LLM systems actually work. You don't need production ML experience. But you need to have built something with these tools — an agent, a RAG pipeline, an internal tool — and to have used evals or experiments to decide whether it was any good. Tokens, context windows, prompt/response tracing, and why an eval suite is fundamental should all be familiar ground. Candidates who have only read about this are not a fit.
