Senior AI Platform Engineer — Cloud & Automation

Aimlroles; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Givzey is seeking an AI Platform Engineer to own and scale our cloud infrastructure, CI/CD pipelines, and AI workflows. You will work across AWS services, IaC tooling, and observability to empower rapid product development.

This role emphasizes reliability, automation, and cross-team collaboration to support autonomous AI capabilities. You will partner with ML engineers to operationalize new AI features, design scalable platforms, and improve deployment and monitoring practices at scale.

Qualifications

• 5+ years building and operating production software systems.
• Strong experience with AWS in production environments.
• Experience designing Infrastructure as Code using Pulumi, Terraform, or CloudFormation.
• Experience building CI/CD pipelines using GitHub Actions.
• Strong Python experience.
• Experience building APIs and backend systems.

Responsibilities

• Design, build, and maintain our AWS infrastructure across environments.
• Own Infrastructure as Code strategy using Pulumi; build reusable components.
• Build and maintain deployment pipelines; improve release automation and safety.
• Develop and operate AI platform infrastructure powering LLM workflows and services.
• Monitor production systems; drive incident response and root-cause analysis.
• Collaborate with product, engineering, ML, and customer success to define standards.

Skills

Production systems

AWS in prod

Infrastructure as code

GitHub Actions

Python

Backend APIs

Tools

Pulumi

Terraform

CloudFormation

Docker

SageMaker

Bedrock

OpenSearch

Job description

Givzey is seeking an AI Platform Engineer to own and scale our cloud infrastructure, CI/CD pipelines, and AI workflows. You will work across AWS services, IaC tooling, and observability to empower rapid product development.

This role emphasizes reliability, automation, and cross-team collaboration to support autonomous AI capabilities. You will partner with ML engineers to operationalize new AI features, design scalable platforms, and improve deployment and monitoring practices at scale.