Description

Must Have Technical/Functional Skills

• Agentic AI: AI agents, planning, reasoning, memory, tool/function calling, multi-agent orchestration, 
• guardrails, and agent evaluation.
• Frameworks: LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent agent 
• orchestration frameworks.
• LLM / GenAI: Azure OpenAI/OpenAI, RAG, embeddings, vector search, hybrid search, 
• prompt engineering, grounding, and hallucination mitigation.
• Microsoft Fabric: Lakehouse, Warehouse, OneLake, Data Factory, Dataflows, Notebooks, 
• Power BI semantic models, Direct Lake, Delta Lake, and medallion architecture.
• Ontology / Semantic Modeling: business entities, relationships, hierarchies, taxonomy, 
• metadata, lineage, semantic layer, knowledge graphs, RDF/OWL/SPARQL or graph databases.
• Engineering: Python, SQL, Spark/PySpark, REST APIs, CI/CD, Git, monitoring, logging, 
• cloud security, and production support.
• Functional: ability to translate business processes into ontology models, semantic 
• data products, and agent workflows

Roles & Responsibilities

• Design and implement enterprise-grade agentic AI solutions that retrieve information, 
• call tools/APIs, reason over data, and automate workflows.
• Integrate AI agents with Microsoft Fabric Lakehouse/Warehouse, semantic models, 
• APIs, enterprise systems, and document repositories.
• Build and optimize RAG pipelines using embeddings, vector/semantic search, structured 
• data grounding, and evaluation datasets.
• Design ontology-driven semantic models covering entities, relationships, metadata, 
• business rules, lineage, and governance.
• Develop Fabric data pipelines using Data Factory, Notebooks, Spark/PySpark, SQL, 
• Delta Lake, and medallion architecture.
• Implement AI guardrails, access control, logging, monitoring, cost optimization, and 
• responsible AI practices.
• Create technical designs, architecture diagrams, reusable components, coding standards etc

Key Skills

Education

Bachelor's degree