About the Role

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

Cloud Infrastructure

• 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).
  

Kubernetes & Container Platform

• 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
  
• Systems Manager (SSM)
  
• CloudFront
  
• EventBridge
  
• SNS
  
• SQS
  

CI/CD & DevOps Automation

• 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
  

Infrastructure as Code

Develop and manage infrastructure using:

• Terraform
  
• AWS CloudFormation
  
• Kubernetes YAML
  

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
  
• Prometheus
  
• Grafana
  
• ELK / OpenSearch
  
• Loki
  

Responsibilities include:

• Infrastructure monitoring
  
• Application monitoring
  
• Log aggregation
  
• Alerting
  
• Capacity planning
  
• Incident response
  

Security & Compliance

• 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
    
• 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.
  

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

Cloud Platforms

• 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
  
• Kubernetes
  
• Amazon EKS
  
• Helm
  
• Kubernetes Networking
  
• Ingress Controllers
  
• Horizontal & Vertical Pod Autoscaling
  

Infrastructure as Code

• Terraform
  
• CloudFormation
  
• Helm
  
• Customize
  

CI/CD

• GitHub Actions
  
• Jenkins
  
• GitLab CI
  
• ArgoCD
  

Databases

• Amazon RDS
  
• PostgreSQL
  
• 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
  

Monitoring & Logging

• Prometheus
  
• 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.
  
• 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.