AI Applications Engineer

Five9

AI Applications Engineer building and operating the cloud platform for AI solutions at Five9. Ensuring secure, reliable, and production-grade infrastructure for AI applications.

Posted 7/23/2026full-timeChennai • 🇮🇳 IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and operating cloud-based AI platforms, with a strong focus on infrastructure as code, CI/CD engineering, and security practices. Proficient in managing production-grade systems and ensuring observability and reliability through effective monitoring and incident response.

Highest-signal resume keywords

Cloud Infrastructure ManagementInfrastructure As Code (Terraform)CI/CD EngineeringMLOps/LLMOps ExperienceSecurity Engineering Fundamentals

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Cloud ComputingInfrastructure As CodeCI/CD PipelinesMLOpsSecurity EngineeringMonitoringLoggingData PipelinesVersion ManagementAgent Runtime

Soft Skills

CollaborationDocumentationProblem-Solving

Tools & Technologies

Google CloudTerraformDockerKubernetes

Certifications & Qualifications

Bachelor's Degree in Computer ScienceEngineering or Related Field

Industry Keywords

AI PlatformProduction SystemsObservabilityIncident ResponseEnterprise Governance

Tech Stack

Tools & technologies

CloudDockerKubernetesTerraform

About the role

Key responsibilities & impact

Build and operate the AI platform on the cloud — compute, environments, networking, and runtime — and keep it production-grade, secure, and reliable.
Provision and manage infrastructure as code (e.g., Terraform) so environments are reproducible, reviewable, and auditable.
Build and maintain the pipelines — build, test, security gates, deploy — that solution builders and business teams ship through.
Stand up model and agent serving, evaluation, prompt and version management, and the tooling to run LLM- and agent-based systems in production.
Build the secure connectors, data pipelines, and integration surface that AI solutions draw on.
Engineer security, data-classification, and responsible-AI controls into the platform (IAM, secrets, egress, policy-as-code) with InfoSec.
Build shared libraries, templates, and self-service tooling so builders and business teams move fast within guardrails.
Instrument monitoring, logging, cost, and SLOs; own platform reliability and incident response.
Partner with the Automation Engineer, the architect, and business builders so the platform meets real build needs; document and support it.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Health insurance
Retirement plans
Paid time off
Flexible work arrangements
Professional development