About the role
Job Role: ML Pipeline Engineer
Duration: 12 Months Contract
Skills Required:
- DevOps, deployment, network operations, systems administration, scripting, coding, development for planning, testing, deployment
- Linux Slackware web servers configuration
- HTML, CSS, JavaScript, Java, C coding
- Android development
- Automation tools
- Cloud infrastructure, CI/CD, automation, security, production operations
- Microsoft Azure with multi subscription architecture, landing zones, governance, policy enforcement
- CI/CD pipelines with GitHub Actions
- Repository migrations, pipeline driven delivery
- Infrastructure as Code, configuration driven deployment, platform standardization
- Linux-based and cloud-hosted application environments administration
- Containerization: Docker, Azure Container Apps, Azure Container Registry
- Networking and secure application delivery: Application Gateway, API Management, SSL/TLS, routing, firewall policies
- Cloud security: Managed Identity, zero secret architecture, RBAC, governance policies, secure access design
- Monitoring with Azure Monitor, Log Analytics, diagnostics, centralized logging, alerting
- Data platform deployment automation: Azure SQL, SQL Managed Instance, Azure Synapse Analytics
- Scripting, provisioning, environment management
- Dev Sec Ops, release governance, compliance cloud operations, operational resilience
- AI/ML platform engineering and MLOps: Azure Machine Learning, Azure OpenAI, Azure AI Search, Azure AI Foundry
- Deploying Azure AI Agents in Docker and MCP servers, AI Search integration, cross tenant connectivity
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