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Cloud Engineer

Job in 201301, Noida, Uttar Pradesh, India
Listing for: Clearwater Analytics
Full Time position
Listed on 2026-06-01
Job specializations:
  • IT/Tech
    Cloud Computing, Systems Engineer
Job Description & How to Apply Below
We are looking for a skilled Cloud Engineer to join our Platform Engineering team and help build, maintain, and scale the secure, reliable, and high-performance cloud infrastructure that powers Clearwater core SaaS platform. You will work hands-on across our multi-account AWS environment, contribute to Amazon EKS platform operations, and collaborate with product teams to deliver robust cloud solutions.

Key Responsibilities:

Cloud Infrastructure & Operations:

Build, deploy, and maintain scalable, secure, and highly available cloud infrastructure on AWS across multi-account environments.
Implement and maintain Infrastructure as Code (IaC) using Terraform and AWS Cloud Formation, contributing to reusable modules used across product teams.
Optimize cloud environments for cost, performance, and reliability, supporting Fin Ops practices including Savings Plans, Spot strategy, and Graviton adoption.
Collaborate with engineering, data, and security teams to support resilient distributed systems.
Participate in continuous improvement initiatives across the platform.
Own incident response: on-call rotation, triage, mitigation, and blameless post-mortems.
Provide Cloud and Infrastructure support across platform teams.

Kubernetes & EKS:

Deploy, operate, and maintain Amazon EKS clusters in a multi-tenant production environment.
Support cluster upgrades, patching, and Kubernetes version lifecycle activities.
Contribute to internal Helm chart libraries and Git Ops-driven cluster configuration using ArgoCD or Flux.

Security & Reliability:

Implement zero-trust network principles and enforce IAM least-privilege across AWS accounts.
Support SRE practices: contribute to SLO definitions and monitoring for EKS, API Gateway, and related services.
Participate in incident response, postmortem analysis, and blameless RCA processes for platform-level issues.
Support chaos engineering exercises and disaster recovery testing across availability zones and regions.

Collaboration & Growth:

Partner with software engineering teams to deliver end-to-end solutions from design through production.
Evaluate new AWS services and open-source tooling to improve infrastructure capabilities.

Required Qualifications:

Hands-on experience with AWS cloud services: EC2, VPC, IAM, EKS, S3, Cloud Watch, API Gateway, Route 53, and more.
Experience operating Amazon EKS in production: cluster lifecycle, RBAC, IRSA, node groups, and autoscaling.
Proficiency in Infrastructure as Code with Terraform and AWS Cloud Formation.
Solid understanding of containerization:
Docker, Kubernetes architecture, and container lifecycle management.

Experience with monitoring and logging tools:
Prometheus, Grafana, Dynatrace, Open Search, ELK/Loki.
Strong Linux/Unix systems administration and scripting in Bash, Python, or similar.
Good knowledge of cloud security best practices: IAM, RBAC, secrets management, and network security.

Experience with Helm and Git Ops tools (ArgoCD, Flux).
Solid networking fundamentals: VPCs, subnets, load balancing, DNS, and Kubernetes ingress controllers.
Ability to troubleshoot distributed systems and debug complex production issues.
Strong problem-solving skills and the ability to work effectively in a fast-paced team environment.

Preferred

Skills:

AWS

Certifications:

Solutions Architect Associate/Professional or Dev Ops Engineer Professional.
Kubernetes

Certifications:

CKA or CKAD.

Experience with Karpenter for EKS node provisioning.
Exposure to microservices architecture and distributed systems at financial-services scale.

Experience with AWS API Gateway and Lambda Authorize rs for JWT/OIDC-based auth flows.
Background in cost optimization and performance tuning (Graviton, Spot, Savings Plans).
Familiarity with identity federation: OIDC, OAuth2, SAML, Auth0 integration.
Understanding of AI/ML infrastructure: model training pipelines, deployment on EKS, and model monitoring.
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