AI Engineer
Summary
Builds and deploys GenAI solutions, agentic workflows, and scalable AI services on Azure, focusing on MLOps/LLMOps, hybrid search, and data quality while integrating React interfaces for AI insights.
- Analyze performance metrics and improve quality
- Apply MLOps and LLMOps best practices
- Build Agentic AI workflows
- Build backend services and APIs
- Design develop deploy GenAI solutions
- Design hybrid search architectures
- Detect data quality anomalies
- Develop interactive React interfaces for AI insights
- Develop secure scalable services on Azure
- Implement AI guardrails and output validation
- Implement automated data quality checks
- Implement identity and access management integration
- Implement rag pipelines
- Implement tool calling and function execution
- Integrate React with AI ML services
- Manage agent memory
- Monitor log trace and observe AI systems
- Optimize prompts retrieval and agent flows
- Orchestrate multi-agent systems
- Perform end-to-end data validation
- Set up model prompt and retrieval testing