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.
