Job Opportunities API

Join us in bringing joy to customer experience.  Five9 is a leading provider of cloud contact center software, bringing the power of cloud innovation to customers worldwide.    Living our values everyday results in our team-first culture and enables us to innovate, grow, and thrive while enjoying the journey together. We celebrate diversity and foster an inclusive environment, empowering our employees to be their authentic selves.  AI Applications Engineer ABOUT THE ROLE Five9 is a leading provider of cloud contact center software, bringing the power of Five9 AI and automation to organizations worldwide. The Enterprise-AI Team drives Five9's internal adoption of AI — turning emerging capability into real business outcomes across the company. The AI Platform Engineer builds and operates the platform Five9's AI solutions run on. Where the AI Automation Engineer builds solutions and automations, you build the paved road they build on — the cloud environment, delivery pipelines, model and agent runtime, data and integration plumbing, security and governance guardrails, and the reusable components that let the team and the business ship AI quickly, safely, and reliably. You keep the platform secure, observable, and production-grade so every AI solution has a dependable foundation, and you stay current on the fast-moving AI infrastructure and tooling landscape. HOW YOU CONTRIBUTE Platform build & operation — Build and operate the AI platform on the cloud — compute, environments, networking, and runtime — and keep it production-grade, secure, and reliable. Infrastructure as code — Provision and manage infrastructure as code (e.g., Terraform) so environments are reproducible, reviewable, and auditable. CI/CD & delivery pipelines — Build and maintain the pipelines — build, test, security gates, deploy — that solution builders and business teams ship through. Model & agent runtime (MLOps / LLMOps) — Stand up model and agent serving, evaluation, prompt and version management, and the tooling to run LLM- and agent-based systems in production. Data & integration plumbing — Build the secure connectors, data pipelines, and integration surface that AI solutions draw on. Security & governance guardrails — Engineer security, data-classification, and responsible-AI controls into the platform (IAM, secrets, egress, policy-as-code) with InfoSec. Reusable components & paved road — Build shared libraries, templates, and self-service tooling so builders and business teams move fast within guardrails. Observability & reliability — Instrument monitoring, logging, cost, and SLOs; own platform reliability and incident response. Enable the builders — Partner with the Automation Engineer, the architect, and business builders so the platform meets real build needs; document and support it. SKILLS, COMPETENCIES & QUALIFICATIONS — REQUIRED Bachelor's degree (or equivalent experience) in Computer Science, Engineering, or a related field. 3+ years in platform, DevOps, infrastructure, or MLOps engineering, including hands-on operation of production cloud systems. Deep hands-on cloud experience (Google Cloud preferred) with infrastructure-as-code (Terraform) and containers / orchestration (Docker, Kubernetes). Strong CI / CD engineering — building delivery pipelines with automated testing and security gates. Experience running ML / LLM or data-intensive systems in production (MLOps / LLMOps: serving, evaluation, versioning, agent runtime). Solid security-engineering fundamentals (IAM, secrets, network egress, policy-as-code) and building to enterprise governance. Observability and reliability practice (monitoring, logging, SLOs, incident response) with a builder-enablement mindset. SKILLS, COMPETENCIES & QUALIFICATIONS — PREFERRED Experience building an internal developer platform or se