Senior DevOps/Cloud Platform Engineer (AWS| Kubernetes|AI Infrastructure)

Annova Solutions Corp.

Indore District

On-site

INR 2,400,000 - 4,200,000

Full time

14 days+
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Job summary

Annova Solutions Corp. is seeking a Senior DevOps / Cloud Platform Engineer with 5–8 years of hands-on experience in designing, deploying, and managing secure, scalable AWS infrastructure.

Expect strong emphasis on EKS, Kubernetes, CI/CD, IaC, and cloud security to support AI workloads and enterprise-scale deployments. You will collaborate with software, AI/ML, and security teams to ensure SOC 2, HITRUST compliant, cost-efficient platforms.

Qualifications

  • Bachelor’s or Master’s degree in Computer Science, IT, or a related field.
  • 5–8 years of hands-on experience in DevOps, Cloud/Platform Engineering.
  • Strong experience designing and managing production AWS environments.

Responsibilities

  • Design, deploy, and manage scalable, highly available, and secure AWS infrastructure.
  • Architect cloud environments capable of supporting enterprise-scale applications and AI workloads.
  • Optimize infrastructure for performance, reliability, scalability, and cost.
  • Implement high availability, disaster recovery, backup, and failover strategies.
  • Design multi-environment infrastructure (Development, QA, UAT, Production).
  • Design, deploy, and manage production-grade Kubernetes clusters using Amazon EKS.

Skills

AWS
Amazon EKS
Kubernetes
CI/CD
Infrastructure as Code
Cloud Security
AI/LLMs

Education

Bachelor’s or Master’s degree in Computer Science/IT

Tools

Terraform
CloudFormation
GitHub Actions
Jenkins
ArgoCD
Docker

Job description

We are looking for a highly skilled Senior DevOps / Cloud Platform Engineer with 5–8 years of experience in designing, deploying, and managing secure, scalable, and highly available cloud infrastructure on AWS.

The ideal candidate will have extensive hands-on experience with AWS, Amazon EKS, Kubernetes, CI/CD automation, infrastructure as code, and cloud security. This role also requires experience supporting AI workloads, including deploying and optimizing Large Language Models (LLMs) on CPU and GPU infrastructure.

You will work closely with software engineers, AI/ML engineers, architects, and security teams to build and maintain cloud platforms that are secure, resilient, cost-efficient, and compliant with SOC 2 and HITRUST requirements.

Key Responsibilities
  • Design, deploy, and manage scalable, highly available, and secure AWS infrastructure.
  • Architect cloud environments capable of supporting enterprise-scale applications and AI workloads.
  • Optimize infrastructure for performance, reliability, scalability, and cost.
  • Implement high availability, disaster recovery, backup, and failover strategies.
  • Design multi-environment infrastructure (Development, QA, UAT, Production).
  • Design, deploy, and manage production-grade Kubernetes clusters using Amazon EKS.
  • Optimize Kubernetes workloads for high availability and resource utilization.
  • Configure namespaces, RBAC, network policies, autoscaling, ingress controllers, and service meshes where applicable.
  • Troubleshoot Kubernetes networking, scheduling, storage, and performance issues.
  • Manage rolling deployments, blue-green deployments, and canary releases.
AWS Services

Strong hands-on experience with:

  • Amazon EKS
  • Amazon EC2
  • Auto Scaling Groups
  • Elastic Load Balancer (ALB/NLB)
  • Amazon S3
  • Amazon RDS
  • AWS Lambda
  • Amazon ECR
  • Amazon CloudWatch
  • IAM
  • Route 53
  • VPC
  • NAT Gateway
  • Security Groups
  • AWS WAF
  • AWS Secrets Manager
  • CloudFront
  • EventBridge
  • SNS
  • SQS
CI/CD & DevOps Automation
  • Design and implement end-to-end CI/CD pipelines.
  • Implement infrastructure automation and GitOps practices.
  • Build deployment strategies with minimal downtime.
  • Integrate automated testing, security scanning, and quality gates into CI/CD pipelines.

Experience with:

  • GitHub Actions
  • Jenkins
  • ArgoCD
Infrastructure as Code

Develop and manage infrastructure using:

  • Terraform
  • AWS CloudFormation
AI & LLM Infrastructure
  • Deploy and manage Small Language Models (SLMs) and Large Language Models (LLMs) in production environments.
  • Build scalable inference infrastructure for AI workloads.
  • Configure GPU-enabled Kubernetes nodes for model serving.
  • Optimize CPU and GPU utilization for AI inference.
  • Manage model deployments, scaling, versioning, and monitoring.
  • Support vector databases and AI inference services.
  • Work closely with AI/ML engineers to optimize model performance and infrastructure costs.
Database Infrastructure & Performance
  • Deploy and manage Amazon RDS databases.
  • Monitor and optimize database performance.
  • Implement backup, recovery, and replication strategies.
  • Tune database configurations for high-throughput applications.
  • Monitor slow queries, indexing strategies, and connection pooling.
  • Collaborate with engineering teams on database performance optimization.
Monitoring & Observability

Implement monitoring and observability using:

  • CloudWatch
  • Grafana
  • ELK / OpenSearch
  • Loki

Responsibilities include:

  • Infrastructure monitoring
  • Application monitoring
  • Alerting
  • Capacity planning
Security & Compliance
  • Implement AWS security best practices.
  • Design secure IAM policies and access controls.
  • Manage secrets and encryption.
Ensure compliance with:
  • SOC 2
  • HITRUST
  • HIPAA
  • Participate in security audits and vulnerability remediation.
  • Maintain audit logs and infrastructure documentation.
Cost Optimization
  • Continuously optimize AWS infrastructure costs.
  • Right-size EC2 instances and EKS node groups.
  • Optimize storage and networking costs.
  • Implement Savings Plans and Reserved Instances where appropriate.
  • Optimize GPU utilization for AI workloads.
  • Monitor cloud spending and recommend cost-saving initiatives.
Requirements
Required Qualifications
  • Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.
  • 5–8 years of hands-on experience in DevOps, Cloud Engineering, or Platform Engineering.
  • Strong experience designing and managing production AWS environments.
  • Extensive experience with Kubernetes and Amazon EKS.
  • Experience managing enterprise-scale cloud infrastructure.
  • Proven experience automating deployments and infrastructure management.
Required Technical Skills
  • Amazon Web Services (AWS)
AWS Services
  • Amazon EC2
  • Amazon EKS
  • Amazon ECS
  • Amazon RDS
  • Amazon S3
  • Lambda
  • ECR
  • CloudFront
  • IAM
  • Route 53
  • VPC
  • CloudWatch
  • Systems Manager
  • WAF
  • Secrets Manager
  • SNS
  • SQS
  • EventBridge
Containers & Orchestration
  • Docker
  • Amazon EKS
  • Ingress Controllers
  • Horizontal & Vertical Pod Autoscaling
Infrastructure as Code
  • Terraform
  • CloudFormation
  • Customize
CI/CD
  • GitHub Actions
  • Jenkins
  • ArgoCD
Databases
  • Amazon RDS
  • MySQL
  • Redis

Experience with:

  • Performance tuning
  • Replication
  • Backup & recovery
  • Connection pooling
  • Query optimization
AI Infrastructure

Experience deploying and managing:

  • LLMs and SLMs
  • GPU-based inference workloads
  • NVIDIA GPU infrastructure
  • CUDA-enabled environments (preferred)
  • Hugging Face models
  • vLLM, Ollama, or similar inference frameworks
  • Model serving and autoscaling
  • Grafana
  • CloudWatch
  • ELK/OpenSearch
  • Loki
Security & Compliance

Strong understanding of:

  • SOC 2
  • HITRUST
  • HIPAA
  • IAM
  • RBAC
  • Network Security
  • Encryption
  • Secrets Management
  • Vulnerability Management
Preferred Qualifications
  • AWS Certified Solutions Architect – Professional or Associate.
  • Experience with AI platforms, MLOps, or GPU infrastructure.
  • Familiarity with service mesh technologies (Istio or Linkerd) is a plus.
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