Cloud-Native Engineer

LiveMindz Inc.; Phoenix (AZ); On-site; USD 110,000 - 160,000; Full time

Job summary

LiveMindz Inc. is seeking an experienced Cloud-Native Engineer to design, build, and modernize cloud-native workloads on AWS and GCP.

The role will focus on containerized deployments with Kubernetes and EKS, along with robust IaC and automated CI/CD pipelines to improve reliability and scalability. Ideal candidates will have hands-on experience in cloud migrations, DevOps automation, and AI-enabled frameworks.

Qualifications

• Hands-on experience designing and developing cloud-native solutions.
• Migrating applications to cloud platforms and automating infrastructure and deployment workflows.
• Exposure to Agentic AI and LLM integration frameworks is an added advantage.

Responsibilities

• Design, develop, and maintain cloud-native solutions on AWS and GCP using modern software engineering practices.
• Design and deploy containerized applications using Kubernetes, Amazon EKS, Docker, and related cloud-native technologies.
• Implement IaC and automate infrastructure provisioning, deployment, and operational workflows.
• Build and maintain CI/CD pipelines using GitHub Actions, Jenkins, and other DevOps toolchains.
• Collaborate with architects, product owners, and application teams to modernize workloads and adopt cloud best practices.
• Troubleshoot technical issues, optimize performance, and improve platform reliability.
• Identify technical debt and drive continuous improvement initiatives.
• Evaluate emerging technologies in Cloud and AI.

Skills

Cloud-native engineering

Python development

DevOps automation

CI/CD pipelines

Tools

AWS

Google Cloud Platform (GCP)

Kubernetes

Docker

Terraform

Infrastructure as Code

GitHub Actions

Jenkins

Job description

We are looking for an experienced Cloud-Native Engineer with strong expertise in Python development, AWS/Google Cloud Platform (GCP), Kubernetes, containerization, DevOps automation, and CI/CD pipelines.

The ideal candidate should have hands-on experience designing and developing cloud-native solutions, migrating applications to cloud platforms, and automating infrastructure and deployment workflows. Exposure to Agentic AI and Large Language Model (LLM) integration frameworks is an added advantage.

Key Responsibilities

• Design, develop, and maintain cloud-native solutions on AWS and Google Cloud Platform using modern software engineering practices.
• Design and deploy containerized applications using Kubernetes, Amazon EKS, Docker, and related cloud-native technologies.
• Implement Infrastructure as Code (IaC) and automate infrastructure provisioning, deployment, and operational workflows.
• Build and maintain CI/CD pipelines using GitHub Actions, Jenkins, and other DevOps toolchains.
• Collaborate with architects, product owners, and application teams to modernize workloads and adopt cloud best practices.
• Troubleshoot technical issues, optimize application performance, and improve platform reliability.
• Identify technical debt and drive continuous improvement initiatives.
• Evaluate emerging technologies in Cloud and AI.

Required Skills

• Hands-on experience with Amazon Web Services (AWS) and/or Google Cloud Platform (GCP).
• Strong knowledge of Kubernetes and containerization.
• Experience with Docker and Amazon EKS.
• Infrastructure as Code (IaC) and Terraform.
• DevOps automation and CI/CD pipeline development.
• Experience with GitHub Actions and Jenkins.
• Cloud application migration and modernization.
• Troubleshooting, performance optimization, and platform reliability.
• Knowledge of modern cloud-native architecture and engineering practices.

Preferred Skills

• Exposure to Agentic AI.
• Knowledge of Large Language Models (LLMs).
• Experience with LLM integration frameworks.
• Familiarity with emerging Cloud and AI technologies.

Key Skills / Keywords

Python, Amazon Web Services, Google Cloud Platform, Kubernetes, Docker, Amazon EKS, Cloud-Native Engineering, Cloud Application Migration, Terraform, Infrastructure as Code, DevOps, CI/CD, GitHub Actions, Jenkins, Containerization, Cloud Automation, Application Modernization, Agentic AI, Large Language Models, LLM Integration, Cloud Architecture, Performance Optimization, Platform Reliability, Continuous Improvement.