Cloud Engineer

Job Summary

We are seeking a Senior Cloud, MLOps & Platform Engineer to own the deployment, automation, monitoring, and operation of enterprise applications and AI platforms across cloud environments. The ideal candidate will be responsible for managing full-stack applications, cloud infrastructure, CI/CD pipelines, AI/ML workloads, and modern web applications while ensuring scalability, security, reliability, and cost efficiency.

Key Responsibilities

• Deploy, manage, and monitor full-stack applications across cloud environments.
• Build and maintain CI/CD pipelines for application and infrastructure deployments.
• Design and manage containerized workloads using Docker, Kubernetes, and GitOps practices.
• Provision and manage cloud infrastructure using Infrastructure as Code (IaC).
• Manage development, testing, staging, UAT, and production environments.
• Deploy, monitor, and support AI/ML workloads and inference services in production.
• Develop, deploy, and maintain frontend applications and integrate them with backend APIs and authentication services.
• Manage databases, cloud storage, backups, disaster recovery, and data governance practices.
• Implement monitoring, logging, security controls, secrets management, and operational best practices.
• Configure and manage networking, reverse proxies, API gateways, and traffic routing.
• Optimize cloud infrastructure, application performance, reliability, and operational costs.
• Collaborate with engineering and AI teams to deliver scalable production solutions.

Required Skills

• 4–8 years of experience in Cloud Engineering, DevOps, Platform Engineering, MLOps, or Full-Stack Engineering.
• Strong hands-on experience with AWS, Azure, or GCP.
• Kubernetes, Docker, Helm, and container orchestration.
• Terraform or similar Infrastructure as Code (IaC) tools.
• Jenkins, GitHub Actions, GitLab CI/CD, or equivalent CI/CD platforms.
• GitOps tools such as ArgoCD or FluxCD.
• Python and Shell scripting.
• Experience with React.js and TypeScript.
• Experience integrating frontend applications with REST APIs, authentication, and authorization workflows.
• Experience building responsive, production-ready web applications.
• Understanding of state management, component-based architecture, and frontend maintainability best practices.
• Experience with reverse proxies, API gateways, cloud networking, IAM, DNS, SSL/TLS, and load balancing.
• Experience with databases, cloud storage, monitoring, observability, and security best practices.
• Experience with secrets management, container registries, and software release management.
• Experience deploying and operating AI/ML workloads in production.
• Understanding of MLOps concepts including model deployment, versioning, lifecycle management, and inference services.
• Strong troubleshooting, automation, and problem-solving skills.

Preferred Skills

• Experience with LLMOps practices.
• Experience with Generative AI applications, RAG architectures, and vector databases.
• Experience with Material UI or similar frontend component libraries.
• Experience supporting AI platforms and inference services.
• Experience working in multi-cloud environments.
• Experience with coding agents (Codex, Claude Code, or Github Copilot) and best practices for token usage.

Ideal Candidate

A hands-on engineer capable of independently managing cloud infrastructure, Kubernetes platforms, CI/CD pipelines, AI/ML workloads, frontend applications, security controls, and production operations. Experience with Generative AI, LLMOps, and modern AI application architectures is highly desirable.