Vconnex Services
CIMET - Senior DevOps Engineer - AWS/Kubernetes
- Compensation
- Not disclosed
- Location
- Gurgaon/Gurugram; Jaipur
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 7+ years
- Category
- Devops
- Posted
Skills
Full description
Purpose Of The Position :
The Senior DevOps Engineer is responsible for designing, building, and scaling CIMET's cloud infrastructure, DevOps platforms, CI/CD pipelines, and MLOps capabilities. The role will drive automation, infrastructure reliability, observability, security, and cloud optimization while ensuring the platform remains highly available, secure, scalable, and compliant with industry standards such as ISO 27001 and SOC2.
Key Accountabilities : Cloud Infrastructure & Automation :
- Design, implement, and manage scalable, secure, and resilient AWS infrastructure.
- Lead Infrastructure-as-Code implementation using Terraform and related automation tools.
- Drive automation across infrastructure provisioning, deployment, monitoring, and operational tasks.
- Implement high-availability and disaster recovery strategies to support business continuity.
CI/CD & Platform Engineering :
- Build and maintain automated CI/CD pipelines to support rapid and reliable deployment.
- Standardize engineering practices, deployment workflows, and release management processes.
- Improve deployment reliability, scalability, and operational efficiency through automation.
- Support engineering teams with platform tooling and DevOps best practices.
Kubernetes & Container Platforms :
- Manage and optimize containerized environments using Docker and Kubernetes (EKS/ECS).
- Maintain platform scalability, reliability, and performance.
- Support large-scale distributed systems and container orchestration strategies.
Observability & Incident Management :
- Develop monitoring, alerting, logging, and incident response frameworks.
- Implement observability solutions using CloudWatch, Grafana, Prometheus, ELK, Datadog, or similar tools.
- Build automated alerting, self-healing mechanisms, and operational runbooks.
- Improve system stability through proactive monitoring and performance analysis.
AI & MLOps Enablement :
- Build and maintain MLOps pipelines supporting model training, deployment, monitoring, and lifecycle management.
- Support AI/ML workloads, model-serving environments, and LLM-powered applications.
- Implement model monitoring, performance tracking, and automated retraining workflows.
- Support secure and scalable AI infrastructure deployment across business platforms.