Azure AI Engineer
Tasks
- Build CI CD pipelines for prompt flows model evaluation and deployment
- Build RAG pipelines with Azure AI Search and Azure OpenAI
- Build evaluation pipelines for safety groundedness relevance and bias
- Configure PII and harmful content filters
- Create infrastructure as code for AI resources with Terraform and Bicep
- Deploy models and AI endpoints on Azure Machine Learning and Azure AI Foundry
- Embed Responsible AI checks into deployment workflows
- Implement AI solutions on Azure AI Foundry
- Implement input output guardrails with Azure AI Content Safety
- Instrument AI workloads with Azure Monitor Application Insights and Log Analytics
- Integrate AI services with applications using REST APIs
- Manage endpoint scaling versioning and traffic routing
Perks/Benefits
Skills/Tech-stack
AI Foundry | AI Search | AI content safety | API Management | Application Insights | Azure AI | Azure AI Content Safety | Azure AI Foundry | Azure AI Search | Azure API | Azure API Management | Azure Container | Azure Container Apps | Azure Data | Azure Data Lake | Azure Data Lake Storage | Azure Data Lake Storage Gen2 | Azure DevOps | Azure Functions | Azure Machine Learning | Azure Monitor | Azure OpenAI | Bicep | CI/CD | Container Apps | Content Safety | Data Lake Storage Gen2 | Delta Lake | Docker | GitHub Actions | Groundedness | Key Vault | LLM safety | Log Analytics | Machine Learning | Managed Identity | Model Evaluation | Private Endpoint | Prompt Shields | Prompt engineering | Python | RBAC | REST APIs | Retrieval-Augmented Generation | Terraform | Token Usage Monitoring | Token usage | Usage monitoring | Vector Databases
Education
N/A
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