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 frameworks.

Network & Security Foundations : Familiar with foundational cloud networking concepts. Programming Languages: Highly proficient in Python (critical for AI/ML environments); familiarity with Groovy, JavaScript, or TypeScript is an asset. Soft Skills: Proven ability to collaborate cross-functionally with Data Scientists, AI Engineers, and Application Developers.

Exceptional analytical, problem-solving, and self-directed execution capabilities. English Skill: Good command of spoken & written (Minimum TOEIC Score 500)