AI Applications Engineer

five9

Bengaluru 3 Years Exp Posted 2h ago

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.