Cloud Infrastructure and DevOps Engineer

Job Summary: Cloud Engineer to lead the design, architecture, and deployment of cloud infrastructure and MLOps environments supporting corporate AI projects and big data analytics. In this role, you will manage high-performance computing clusters (AI Compute Clusters), optimize model lifecycle pipelines from staging to production, and ensure a highly secure, resilient, and cost-efficient cloud platform. Responsibilities: Cloud Architecture & Infrastructure : Design and develop cloud-native, secure, and scalable infrastructures (primarily on Microsoft Azure) tailored for AI model processing, deep learning platforms, and AI-driven applications. Network & Cloud Security : Design and implement enterprise-grade network security, including Azure Landing Zone (Hub-Spoke architecture), and manage firewall policies (Azure Firewall, NSG, Route Table) for service access and endpoint restrictions. End-to-End MLOps Workflows: Build and maintain comprehensive MLOps workflows, managing the model lifecycle (Model Lifecycle Management), model tracking, and production deployments using tools like MLflow or Azure Machine Learning. Compute Optimization: Manage and optimize autoscaling mechanisms for AI compute worker nodes, effectively handling scale computing tasks without performance degradation. Pipelines & Automation: Develop Infrastructure as Code (IaC) via Terraform and automate CI/CD pipelines triggered by APIs to dynamically provision compute infrastructure for advanced mathematical/AI solutions. Secure Environments: Establish secure enterprise network zones (Azure Landing Zone) and seamlessly migrate legacy AI workloads into environments aligned with corporate IT security standards. Identity & Governance: Govern identity, security compliance, and access controls (Microsoft Entra ID, Managed Identity) for internal/external API integrations with AI endpoints (e.g., Azure Machine Learning). Financial & Cloud Cost Optimization: Monitor, analyze, and optimize cloud infrastructure costs. Generate financial reports and manage resource budgeting to ensure maximum cost-efficiency for AI workloads. Qualifications: Experience: 5+ years of hands-on experience in Cloud Infrastructure, DevOps, or AI/Data Engineering , with at least 2-3 years dedicated to building AI infrastructure or MLOps platforms. AI & Cloud Expertise: Strong proficiency in AI-related cloud ecosystem components (Azure), such as Azure ML, Azure OpenAI, Data Factory, Container Apps, and serverless architectures. MLOps & Containerization Skills: Hands-on experience with MLflow and Docker to effectively containerize and orchestrate Machine Learning models. DevOps & IaC Expertise: Advanced skills in writing declarative Terraform scripts and constructing automation pipelines using Azure DevOps or Jenkins . Data & Frameworks Foundation: Solid understanding of data architecture, database management (SQL/NoSQL), and integration layers including REST APIs and GraphQL…