AI and Fabric Engineer

Job 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.