Required Skills & Expertise

Agentic Layer & Protocols: Hands-on expertise with Agentic Layer A2A frameworks and MCP Protocol for multi-agent orchestration.
AI/ML Engineering: Strong background in vector embeddings, prompt engineering, context engineering, and fine-tuning LLMs.
GenAI & LLM Concepts: Deep understanding of Generative AI, Natural Language Models (NLM), and Large Language Models (LLM).
Programming: Advanced proficiency in Python; exposure to Java/Go is a plus.
Cloud Proficiency: Strong experience with Azure Cloud services, including deployment, monitoring, and scaling.
Databases: Expertise in Azure AI Search, Redis, Cosmos DB; familiarity with Blob Storage and Iceberg is advantageous.
Cloud-Native Architecture: Solid grasp of microservices, containerization, serverless computing, scalability, and performance optimization.
Healthcare Domain: Experience working with regulated data environments and compliance frameworks.

Evaluation Criteria (Critical Components)

1. Technical Depth

· Ability to design and implement multi-agent AI systems.

· Experience in LLM fine-tuning, embeddings, and context engineering.

· Expertise in coding proficiency with production-grade systems in Python.

2. Architectural Vision

· Ability to define enterprise-level AI/ML architecture aligned with cloud-native principles.

· Experience in scalability, resilience, and performance optimization.

3. Cloud & Data Expertise

· Hands-on deployment of AI workloads on Azure Cloud.

· Strong knowledge of databases, search systems, and distributed storage.

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