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 RequirementsMust-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:
• Experience configuring Azure Virtual Networks (VNets), subnets, and network security rules.
• Equivalent experience with AWS VPCs or GCP VPCs is acceptable.
• Strong understanding of IAM principles, with experience implementing least-privilege access controls in Azure IAM (preferred), or equivalent in AWS IAM or GCP IAM.
• 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.