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Introduction to DemandbaseDemandbase 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, were 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 RoleAI 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 ownThe 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 needStrong 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.Nice to haveAn LLM gateway or proxy (LiteLLM or similar) in production.LLM observability tooling: Datadog LLM Observability, LangSmith, Braintrust, Arize, or similar.Eval frameworks and LLM-as-judge scoring in a real workflow, not a demo.FinOps instincts cost attribution, showback, quota design.Guardrails, PII detection, or content-filtering systems.GCP alongside AWS.Our stackAWS - GCP EKS, Flux/GitOps, Karpenter Python LiteLLM Datadog (LLM observability, APM) Prometheus, Loki, ClickHouse Terraform GitLab CIBenefitsOur benefits include Group Medical, Personal Accident, and Term Life Insurance for comprehensive protection. Preventive healthcare covers dental, vision, and OPD needs, complemented by strong mental health support. We also provide a fitness benefit, car lease policy, and gratuity for long-term financial well-being. Our Commitment to Diversity, Equity, and Inclusion at DemandbaseAt Demandbase, we believe in creating a workplace culture that values and celebrates diversity in all its forms. We recognize that everyone brings unique experiences, perspectives, and identities to the table, and we are committed to building a community where everyone feels valued, respected, and .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.