Noida, Uttar Pradesh
Job Summary

About the Role

We are looking for an experienced DevOps Engineer to design, automate, secure, and manage cloud-native infrastructure and deployment platforms. AWS experience is mandatory, with hands-on exposure to GenAI service integrations and working knowledge across Azure, Google Cloud Platform, and other modern cloud platforms. The role requires strong capability in CI/CD automation, Infrastructure as Code, container orchestration, observability, DevSecOps, and production-grade cloud operations.

This role is suitable for candidates with 5 to 12 years of relevant experience in DevOps, cloud engineering, platform engineering, infrastructure automation, or site reliability engineering.

Key Responsibilities

Key Responsibilities

Design, implement, and manage scalable, secure, and highly available cloud infrastructure.
Build and maintain CI/CD pipelines for application, infrastructure, container, and GenAI-enabled workloads.
Automate infrastructure provisioning and configuration using Terraform, CloudFormation, CDK, Ansible, or equivalent tools.
Deploy, manage, and optimize containerized workloads using Docker, Kubernetes, Amazon EKS, ECS, or equivalent platforms.
Support integration of GenAI services such as Amazon Bedrock, SageMaker, model APIs, vector databases, prompt management, and AI application deployment workflows.
Implement monitoring, logging, alerting, tracing, and observability using CloudWatch, Prometheus, Grafana, ELK, OpenTelemetry, or similar tools.
Apply DevSecOps practices including IAM governance, secrets management, vulnerability scanning, policy enforcement, and secure deployment controls.
Manage cloud networking, load balancing, DNS, VPN, private connectivity, firewalls, and environment segregation across cloud platforms.
Drive reliability, scalability, performance tuning, cost optimization, backup, disaster recovery, and production support activities.
Collaborate with development, QA, security, architecture,



and operations teams to improve release velocity and platform stability.
Skill Requirements

Mandatory Technical Skills

Mandatory hands-on experience with AWS cloud services , including compute, storage, networking, security, monitoring, and deployment services.
Strong experience in AWS services such as EC2, S3, VPC, IAM, Lambda, API Gateway, RDS, CloudWatch, CloudTrail, ECS, EKS, CodePipeline, CodeBuild, and CodeDeploy.
Hands-on exposure to GenAI service integrations using Amazon Bedrock, SageMaker, model endpoints, APIs, embedding services, vector databases, and AI application deployment pipelines.
Strong knowledge of CI/CD tools such as Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps, AWS CodePipeline, or similar platforms.
Strong experience with Infrastructure as Code using Terraform, AWS CloudFormation, AWS CDK, Ansible, or equivalent automation frameworks.
Hands-on experience with Docker, Kubernetes, Helm, Amazon EKS, ECS, and container registry management.
Working knowledge of multiple cloud platforms, including AWS, Microsoft Azure, Google Cloud Platform, and hybrid or multi-cloud deployment models.
Solid scripting and automation skills using Python, Bash, PowerShell, or equivalent scripting languages.
Strong understanding of Linux administration, networking fundamentals, DNS, load balancers, firewalls, SSL/TLS, and cloud security controls.
Experience with monitoring, observability, log management, incident response, and production support for enterprise applications.

Preferred / Additional Skills

Experience implementing GenAIOps practices such as prompt versioning, model configuration deployment,



evaluation workflows, guardrails, and AI workload monitoring.
Exposure to vector databases, RAG pipelines, API-based LLM integrations, AI gateways, and secure GenAI workload orchestration.
Knowledge of Azure DevOps, Azure Kubernetes Service, Azure Monitor, Google Kubernetes Engine, Cloud Build, and Google Cloud Operations Suite.
Experience with DevSecOps tools such as SonarQube, Snyk, Checkmarx, Trivy, Aqua, Prisma Cloud, or equivalent security platforms.
Familiarity with service mesh, API management, event-driven architecture, serverless deployment, and microservices operations.
AWS, Azure, Google Cloud, Kubernetes, Terraform, DevOps, or security certifications are preferred.
Other Requirements

Experience Criteria

5 to 12 years of relevant experience in DevOps engineering, cloud infrastructure, platform engineering, SRE, automation, or production operations.
Candidates should have strong hands-on experience in AWS-based production environments, with practical knowledge of Azure, Google Cloud Platform, and multi-cloud architecture.
Candidates should have experience supporting enterprise-scale applications, cloud migration, automation, deployment governance, security compliance, and production incident management.

Educational Qualifications

Mandatory Qualification:
B.E. / B.Tech in Computer Science, Information Technology, Electronics, Software Engineering, or any other relevant engineering stream.

Equivalent qualifications may also be considered:
BCA / MCA / M.Tech / M.Sc. in Computer Science, Information Technology, Cloud Computing, Cybersecurity, Software Engineering, or related disciplines from a recognized institution or university.
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