Senior Cloud Engineer – GenAI
4 days ago
We are seeking a Cloud Engineer to design and implement scalable, cloud-native workflows for GenAI, AI, and ML systems. This role focuses on building automated, event-driven pipelines using Apache Airflow, Azure Functions, and EventGrid, with full observability through Azure-native monitoring tools. The ideal candidate will have experience integrating these components into secure, production-grade environments using Infrastructure-as-Code (IaC) and CI/CD practices.Job Requirements
Must-Have Skills & Experience
Workflow Orchestration: Experience designing and managing end-to-end GenAI/AI/ML workflows using Apache Airflow or equivalent.
Event-Driven Architecture: Proficiency in building event-driven systems using Azure Functions and EventGrid. Equivalent experience with AWS Lambda + EventBridge or GCP Cloud Functions + Eventarc is acceptable.
Monitoring & Observability: Hands-on experience implementing monitoring, logging, and alerting using Azure Log Analytics, Azure Monitor, and Alerts. Equivalent experience with AWS CloudWatch or GCP Operations Suite is acceptable.
CI/CD Integration: Experience integrating workflow components into CI/CD pipelines using Azure DevOps Pipelines (preferred), or equivalent tools such as AWS CodePipeline or Google Cloud Build.
Infrastructure as Code (IaC): Proficiency in managing cloud infrastructure using Terraform, preferably on Azure. Equivalent experience on AWS or GCP is acceptable.
Cloud Services Integration: Familiarity with cloud-native services such as Blob Storage, Data Lake, SQL Database, Cosmos DB, and Key Vault in Azure. Equivalent services in AWS or GCP are acceptable.
Networking & Security:
o Experience configuring Azure Virtual Networks (VNets), subnets, and network security rules. Equivalent experience with AWS VPCs or GCP VPCs is acceptable.
o Strong understanding of IAM principles, with experience implementing least-privilege access controls in Azure IAM (preferred), or equivalent in AWS IAM or GCP IAM.
o Experience implementing secure cloud architectures following industry best practices and compliance standards (e.g., SOC2, HIPAA, GDPR).Good-to-Have Skills
Cloud Cost Optimization: Experience in tuning infrastructure and automating resource management to reduce operational costs.
Multi-Cloud Experience: Proficiency in Azure (preferred), with working knowledge of AWS or GCP.
Containerization & Orchestration: Familiarity with Docker and Kubernetes, preferably using Azure Kubernetes Service (AKS). Equivalent experience with Amazon EKS or Google Kubernetes Engine (GKE) is acceptable.
MLOps & LLMs: Familiarity with MLOps practices and experience working with Large Language Models (LLMs). Collaboration & Communication
Ability to work cross-functionally with data scientists, ML engineers, and DevOps teams.
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