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.

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
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.

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