AI Solutions :

- Compulsory knowledge of AI Solutions like LLM usage, Agentic frameworks, RAG, MCP, etc.

Data Handling :

- Should know how to handle data in databases and graphs, such as Azure Databases, Azure Cosmos Graph DB, etc.

Python Proficiency :

- Should have high coding proficiency in Python.

Application Deployment :

- Must have good application deployment knowledge.

- Should have experience with taking applications from scratch to production.

Optimization Techniques :

- Must know optimization techniques to improve deployment capabilities, like reducing latency, reducing computation resources, etc.

Azure Tools :

- Should know usage of Azure AI Tools and other Azure features.

Key Responsibilities :

- Architect and implement robust RAG (Retrieval-Augmented Generation) pipelines to improve the accuracy and context-awareness of generative models for end-users.

- Integrate Model Context Protocol (MCP) standards to streamline data exchange and enhance interoperability across our AI service ecosystem.

- Develop and optimize high-performance Python-based applications that interface seamlessly with large-scale data infrastructures.

- Manage and scale data storage solutions using Cosmos DB, ensuring low-latency access and high availability for AI-driven features.

- Deploy and monitor AI models within the Azure AI ecosystem to ensure reliability, security, and cost-efficiency for our global client base.

- Collaborate with engineering leads to refine model performance and iterate on features based on real-time user feedback and system metrics.