Senior Cloud Engineer – GenAI / AI/ML

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 SkillsCloud 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 & CommunicationAbility to work cross-functionally with data scientists, ML engineers, and DevOps teams.