Generative AI Engineer

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.