DevOps Azure/AWS

We are seeking a Senior DevOps Engineer with 7+ years of experience managing cloud infrastructure and CI/CD pipelines across Azure and AWS. The ideal candidate has mandatory hands-on AI/ML experience (3+ years) along with mandatory Java and Spring Boot skills, enabling them to support and deploy intelligent, backend-driven applications at scale., * Design, implement, and manage CI/CD pipelines across Azure DevOps, AWS CodePipeline, Jenkins, or GitHub Actions

• Architect and maintain cloud infrastructure on Azure and AWS (compute, networking, storage, security)
• Support deployment and scaling of Java/Spring Boot microservices and applications
• Enable and support MLOps workflows deploying, monitoring, and scaling AI/ML models in production
• Build and maintain Infrastructure as Code (Terraform, ARM/Bicep, CloudFormation)
• Implement containerization and orchestration strategies (Docker, Kubernetes, AKS, EKS)
• Set up monitoring, logging, and alerting (Prometheus, Grafana, CloudWatch, Azure Monitor)
• Collaborate with development, data science, and QA teams to streamline release cycles
• Drive automation of build, test, and deployment processes to improve reliability and speed
• Ensure security best practices, compliance, and cost optimization across cloud environments
• Troubleshoot production incidents and perform root-cause analysis

• 7+ years of DevOps experience with strong hands-on expertise in Azure and AWS
• Mandatory: 3+ years of AI/ML experience supporting ML pipelines, model deployment, or MLOps workflows in production environments
• Mandatory: Strong Java and Spring Boot skills understanding application architecture, build/deployment requirements, and troubleshooting at the code level
• Strong experience with CI/CD tools (Azure DevOps, Jenkins, GitHub Actions, AWS CodePipeline)
• Proficiency in Infrastructure as Code (Terraform, CloudFormation, ARM/Bicep templates)
• Hands-on experience with containerization and orchestration (Docker, Kubernetes, AKS, EKS)
• Scripting experience (Python, Bash, PowerShell) for automation
• Experience with monitoring/logging tools (Prometheus, Grafana, ELK, CloudWatch, Azure Monitor)
• Solid understanding of networking, security, and identity/access management in cloud environments
• Experience with version control (Git) and artifact repositories (Nexus, Artifactory, ECR, ACR)
• Strong troubleshooting, collaboration, and communication skills, * Azure/AWS certifications (Azure DevOps Engineer Expert, AWS Certified DevOps Engineer)
• Experience with MLOps platforms (MLflow, Kubeflow, SageMaker Pipelines, Azure ML)
• Familiarity with message queues/event streaming (Kafka, RabbitMQ, Azure Service Bus, SQS)
• Experience with GitOps tools (ArgoCD, FluxCD)
• Exposure to microservices deployment patterns and service mesh (Istio, Linkerd), * Multi-cloud deployment strategy and hybrid cloud management
• Cost optimization and FinOps practices across Azure/AWS
• Serverless architecture (AWS Lambda, Azure Functions)
• Secrets management (HashiCorp Vault, Azure Key Vault, AWS Secrets Manager)
• Chaos engineering / resilience testing tools

AI/ML Related

• Experience deploying containerized ML models (TorchServe, TensorFlow Serving, KFServing)
• Familiarity with vector databases and RAG-based application deployment
• Understanding of GPU-based compute provisioning for ML workloads (AWS SageMaker, Azure ML Compute)
• Model monitoring/observability (drift detection, performance tracking)

Java / Spring Boot

• Experience with Spring Cloud (config server, service discovery, gateway)
• Understanding of JVM performance tuning and profiling
• Familiarity with build tools (Maven, Gradle) and dependency management

Soft Skills

• Strong cross-functional collaboration with development and data science teams
• Incident management and on-call rotation experience
• Documentation and knowledge-sharing practices