This role is for one of Weekday’s clients

Min Experience: 5+ years
Location: Mumbai, Maharashtra, India
JobType: full-time

Must-have Skills: DevOps, Cloud Infrastructure, Kubernetes
Key Technologies: Docker, Jenkins, Terraform, GCP, GKE, Cloud SQL, Redis, Pub/Sub, Vertex AI, Linux, Grafana, Prometheus, Thanos, Shell Scripting, Python

About the Role

We are looking for an experienced Lead DevOps Engineer to lead the design, implementation, and optimization of scalable, reliable, and secure cloud infrastructure. The ideal candidate will have strong hands-on expertise in DevOps, cloud infrastructure, Kubernetes, containerization, and infrastructure automation. You will be responsible for managing cloud-native environments, building robust CI/CD pipelines, improving system reliability, and enabling seamless application deployments.

This role requires a strong understanding of Google Cloud Platform (GCP), infrastructure as code, monitoring, and observability. You will collaborate with engineering and development teams to streamline deployment processes, enhance operational efficiency, and ensure high availability across production systems.

Requirements

Key Responsibilities

• Cloud Infrastructure Management: Design, deploy, and maintain scalable cloud infrastructure on Google Cloud Platform (GCP), leveraging services such as Google Kubernetes Engine (GKE), Cloud SQL, Redis, Pub/Sub, virtual machines, and Vertex AI.
• Kubernetes and Containerization: Manage Kubernetes clusters and Docker-based applications, ensuring efficient container orchestration, resource utilization, scalability, and high availability.
• CI/CD Pipeline Development: Build, maintain, and optimize continuous integration and continuous deployment pipelines using Jenkins and related automation tools to enable reliable and frequent software releases.
• Infrastructure as Code: Implement and manage infrastructure provisioning, configuration, and lifecycle automation using Terraform, following Infrastructure as Code (IaC) best practices.
• Monitoring and Observability: Develop and maintain monitoring and alerting solutions using Grafana, Prometheus, and Thanos to track system health, identify performance bottlenecks, and troubleshoot production issues.
• Linux and Networking: Apply strong Linux administration and networking fundamentals to manage servers, troubleshoot connectivity issues, optimize performance, and maintain infrastructure security.
• Scripting and Automation: Develop automation scripts using Shell and Python to simplify repetitive operational tasks, improve deployment workflows, and reduce manual intervention.
• Reliability and Security: Establish best practices for infrastructure security, access management, backup strategies, disaster recovery, and incident response.
• Technical Leadership: Guide DevOps initiatives, collaborate with cross-functional teams, conduct technical reviews, and drive continuous improvement across infrastructure and deployment processes.

Must-Have Skills and Qualifications

• 5–9 years of hands-on experience in DevOps and cloud infrastructure engineering.
• Strong practical expertise in Kubernetes and Docker for container orchestration and application deployment.
• Experience building and managing CI/CD pipelines using Jenkins.
• Proficiency in Terraform and Infrastructure as Code practices.
• Hands-on experience with GCP services, particularly GKE, Cloud SQL, Redis, Pub/Sub, virtual machines, and Vertex AI.
• Strong knowledge of Linux administration, system troubleshooting, and networking fundamentals.
• Experience with monitoring and observability tools, including Grafana, Prometheus, and Thanos.
• Proficiency in Shell scripting and Python for automation and operational efficiency.
• Strong problem-solving, communication, collaboration, and technical leadership skills.

Good-to-Have Skills

• Experience managing production-grade, distributed, cloud-native systems.
• Knowledge of cloud security, disaster recovery, and high-availability architecture.
• Familiarity with performance optimization, incident management, and cost optimization on GCP.
• Experience working with AI/ML infrastructure and cloud-based AI services.