DevOps Engineer

Job Summary

We are looking for a skilled Cloud / DevOps Infrastructure Engineer to design, implement, automate, and maintain scalable and secure cloud infrastructure across AWS, Azure, and/or GCP.

The ideal candidate will have strong hands-on experience with Kubernetes, Docker, Terraform, CI/CD, Linux, cloud infrastructure, networking, security, monitoring, and troubleshooting. Experience supporting AI/ML workloads, GPU infrastructure, or high-performance computing environments will be an added advantage.

The candidate will work closely with development, engineering, security, and operations teams to build reliable infrastructure and automate deployment and operational processes.

Key Responsibilities

• Design, deploy, and manage cloud infrastructure across AWS, Azure, and/or GCP.
• Build and manage containerized applications using Docker and Kubernetes.
• Develop and maintain Infrastructure as Code (IaC) using Terraform.
• Design and maintain CI/CD pipelines for automated application deployment.
• Manage source control and development workflows using Git.
• Provision, configure, and maintain Linux-based servers and environments.
• Implement cloud infrastructure automation to improve scalability, reliability, and operational efficiency.
• Configure and maintain cloud networking components including VPC/VNet, subnets, routing, load balancers, DNS, firewalls, and security groups.
• Apply cloud security best practices including IAM, access control, secrets management, encryption, and network security.
• Monitor infrastructure, applications, and services using appropriate monitoring and logging tools.
• Troubleshoot infrastructure, networking, deployment, performance, and production issues.
• Support high-availability, scalability, backup, disaster recovery, and business continuity requirements.
• Collaborate with application developers and engineering teams to improve deployment and infrastructure processes.
• Identify opportunities to automate repetitive operational tasks.
• Participate in incident management, root-cause analysis, and resolution of production issues.
• Maintain infrastructure documentation, deployment procedures, and operational runbooks.
• Implement best practices for cloud cost optimization, resource utilization, and infrastructure performance.

Required Technical Skills

Cloud Platforms

• Hands-on experience with one or more:
• AWS
• Microsoft Azure
• Google Cloud Platform (GCP)

Containers & Orchestration

• Strong experience with Docker.
• Hands-on experience with Kubernetes.
• Knowledge of Kubernetes deployments, services, ingress, config maps, secrets, namespaces, and scaling.

Infrastructure as Code

• Strong experience with Terraform.
• Experience creating and managing reusable infrastructure modules.
• Understanding of Infrastructure as Code principles and automated provisioning.

CI/CD & Version Control

• Experience building and maintaining CI/CD pipelines.
• Strong knowledge of Git and Git-based development workflows.
• Experience with tools such as Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps, or similar platforms.

Linux

• Strong Linux administration and troubleshooting skills.
• Experience with shell scripting and system-level troubleshooting.
• Understanding of processes, services, permissions, networking, storage, and system performance.

Networking & Security

• Strong understanding of:
• TCP/IP
• DNS
• HTTP/HTTPS
• Load Balancing
• Firewalls
• VPN
• VPC/VNet
• Subnets
• Routing
• Security Groups / Network ACLs
• IAM and access management

Monitoring & Troubleshooting

• Experience with infrastructure and application monitoring.
• Knowledge of logging, alerting, metrics, and performance monitoring.
• Ability to troubleshoot production issues across cloud, networking, Kubernetes, Linux, and application infrastructure.

Preferred / Good-to-Have Skills

• Experience supporting AI/ML infrastructure.
• Exposure to GPU infrastructure and GPU-enabled workloads.
• Experience with NVIDIA GPUs, CUDA, or GPU scheduling is a plus.
• Experience deploying and managing ML/AI workloads on Kubernetes.
• Knowledge of MLOps or AI platform infrastructure.
• Experience with Helm and Kubernetes package management.
• Experience with Prometheus, Grafana, ELK/EFK, Datadog, CloudWatch, Azure Monitor, or similar tools.
• Experience with Python, Bash, or other scripting languages.
• Knowledge of cloud cost optimization and FinOps practices.

Qualifications

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
• 4–8 years of experience in Cloud, DevOps, Infrastructure, SRE, or related engineering roles.
• Strong problem-solving and troubleshooting skills.
• Ability to work independently as well as collaboratively with cross-functional teams.
• Strong communication and documentation skills.

Ideal Candidate Profile

The ideal candidate is a hands-on infrastructure engineer who can build, automate, secure, monitor, and troubleshoot modern cloud environments. The candidate should be comfortable working across cloud platforms, Kubernetes, Terraform, CI/CD, Linux, networking, and security.

Experience with AI/ML platforms or GPU-based infrastructure will be highly valuable for supporting next-generation AI workloads.