Senior DevOps Cloud Platform Engineer AWS Kubernetes AI Infrastructure
Annova Solutions India
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Job summary
Annova Solutions India in Indore is seeking a Senior DevOps/Cloud Platform Engineer to design, deploy and manage scalable, secure cloud infrastructure on AWS, including EKS and Kubernetes, with CI/CD automation. The role focuses on building production-ready environments for enterprise apps and AI workloads.
You will collaborate with software, AI/ML, architecture and security teams to ensure cost-efficient, resilient platforms that meet SOC 2 and HITRUST requirements, using IaC and modern DevOps
Qualifications
- Bachelor's or Master's in CS/IT or related field.
- 5–8 years of hands-on DevOps/Cloud Platform experience.
- Strong experience with AWS, EKS, Kubernetes.
- Proficient in CI/CD, IaC, and security best practices.
Responsibilities
- Design, deploy, and manage scalable AWS infrastructure.
- Operate Kubernetes/EKS clusters and CI/CD pipelines.
- Implement IaC with Terraform/CloudFormation.
- Support AI workloads with GPU/CPU infrastructure.
- Ensure SOC 2, HITRUST, HIPAA compliance.
Skills
Education
Tools
Job description
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.
- 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.
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
- Systems Manager (SSM)
- CloudFront
- EventBridge
- SNS
- SQS
- Design and implement end-to-end CI/CD pipelines.
- Automate application deployments across multiple environments.
- 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
- GitLab CI
- ArgoCD
Develop and manage infrastructure using:
- Terraform
- AWS CloudFormation
- Kubernetes YAML
- 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.
- 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.
Implement monitoring and observability using:
- CloudWatch
- Prometheus
- Grafana
- ELK / OpenSearch
- Loki
Responsibilities include:
- Infrastructure monitoring.
- Application monitoring.
- Log aggregation.
- Alerting.
- Capacity planning.
- Incident response.
- Implement AWS security best practices.
- Design secure IAM policies and access controls.
- Manage secrets and encryption.
- Perform infrastructure hardening.
- Ensure compliance with:
- SOC 2
- HITRUST
- HIPAA
- SOC 2
- Participate in security audits and vulnerability remediation.
- Maintain audit logs and infrastructure documentation.
- 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.
- Bachelors or Masters 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.
- Amazon Web Services (AWS)
- 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
- Docker
- Kubernetes
- Amazon EKS
- Helm
- Kubernetes Networking
- Ingress Controllers
- Horizontal & Vertical Pod Autoscaling
- Terraform
- CloudFormation
- Helm
- Customize
- GitHub Actions
- Jenkins
- GitLab CI
- ArgoCD
- Amazon RDS
- PostgreSQL
- MySQL
- Redis
Experience with:
- Performance tuning
- Replication
- Backup & recovery
- Connection pooling
- Query optimization
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
- Prometheus
- Grafana
- CloudWatch
- ELK/OpenSearch
- Loki
Strong understanding of:
- SOC 2
- HITRUST
- HIPAA
- IAM
- RBAC
- Network Security
- Encryption
- Secrets Management
- Vulnerability Management
- AWS Certified Solutions Architect – Professional or Associate.
- AWS Certified DevOps Engineer – Professional.
- Certified Kubernetes Administrator (CKA) or Certified Kubernetes Application Developer (CKAD).
- Experience with AI platforms, MLOps, or GPU infrastructure.
- Experience deploying high-availability, multi-tenant SaaS applications.
- Familiarity with service mesh technologies (Istio or Linkerd) is a plus.
Requirements:
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