Job Summary (List Format):

- End-to-End Platform Ownership: Manage and optimize cloud (Azure), DevOps, and AI/ML infrastructure for production-grade .NET 10 microservices and Python-based AI/ML ecosystem.
- Azure Infrastructure: Provision, configure, and maintain a wide range of Azure resources (App Services, Entra ID, PostgreSQL, Service Bus, Redis, Key Vault, AKS, Virtual Networks, Load Balancers, etc.), ensuring scalability, security, and cost optimization.
- Hybrid/On-Premises Management: Support on-prem SQL Server, IIS/Windows Server, MongoDB, VPN/ExpressRoute, and Kafka/Confluent clusters, maintaining secure hybrid connectivity.
- DevOps & CI/CD Engineering: Design, implement, and manage robust CI/CD pipelines (Azure DevOps, YAML, GitHub Actions), automate builds/deployments for .NET and Python, enable release strategies (Blue/Green, Canary), and integrate security and infrastructure-as-code (Terraform/Bicep).
- AI/ML Application & MLOps: Deploy and manage Python-based AI/ML services (FastAPI/Flask), oversee the end-to-end ML model lifecycle (training, validation, deployment, monitoring), containerize and deploy ML workflows, integrate with .NET microservices and event streaming platforms, and ensure reproducibility and traceability.
- Observability & Logging: Implement and manage centralized monitoring and logging systems (Elasticsearch, Kibana,



Azure Monitor, App Insights), build dashboards for infrastructure, services, and model performance, and maintain structured logging.
- Security & Compliance: Enforce security best practices across cloud/on-prem environments, including RBAC, certificate management, endpoint protection, secret rotation, access audits, and secure model deployment.
- Collaboration & Automation: Foster automation, scalability, and zero-downtime deployment across all layers, working cross-functionally with development and data science teams.
- Required Skills: 4+ years' Azure experience, deep knowledge of Azure DevOps, AKS, App Services, .NET and Python application lifecycle, MLOps tools (MLflow, Azure ML), databases (SQL, PostgreSQL, MongoDB, Redis), containerization (Docker, Kubernetes), messaging (Kafka, Service Bus), and observability (ELK Stack).
- Preferred/Nice to Have: Azure ML, Confluent Cloud, workflow orchestration tools, AWS exposure, deep OAuth2/OIDC, and certifications such as AZ-104, AZ-204, AZ-400, DP-100, CKA.
- What You Will Own: The full hybrid cloud/on-prem platform, CI/CD for 25+ projects and ML workflows, MLOps lifecycle, centralized observability, and secure, scalable, high-availability deployments for both microservices and AI/ML systems.

📌 Azure Infrastructure, DevOps (India)
🏢 Sparix Global
📍 India