Sr. Devops or Cloud Engineer

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).

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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.

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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

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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

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Infrastructure as Code

Develop and manage infrastructure using:

• Terraform
• AWS CloudFormation
• Kubernetes YAML

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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.

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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.

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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

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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.

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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.

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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.

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Required Technical Skills

Cloud Platforms

• Amazon Web Services (AWS)

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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

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Containers & Orchestration

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

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Infrastructure as Code

• Terraform
• CloudFormation
• Helm
• Kustomize

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CI/CD

• GitHub Actions
• Jenkins
• GitLab CI
• ArgoCD

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Databases

• Amazon RDS
• PostgreSQL
• MySQL
• Redis

Experience with:

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

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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

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Monitoring & Logging

• Prometheus
• Grafana
• CloudWatch
• ELK/OpenSearch
• Loki

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Security & Compliance

Strong understanding of:

• SOC 2
• HITRUST
• HIPAA
• IAM
• RBAC
• Network Security
• Encryption
• Secrets Management
• Vulnerability Management

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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.