AI Knowledge Engineer

Key Responsibilities

· Create and implement enterprise-level taxonomies, ontologies, metadata frameworks, and semantic models.

· Develop and sustain knowledge graphs along with semantic search infrastructures.

· Design and refine RAG, Agentic RAG, and GraphRAG solutions.

· Formulate strategies for chunking, embedding, indexing, retrieval, reranking, and grounding.

· Deploy vector databases and retrieval pipelines tailored for both structured and unstructured content.

· Construct Python-driven ingestion, enrichment, and knowledge pipelines on cloud platforms.

· Establish standards for knowledge quality, governance, lineage, discoverability, and lifecycle management.

· Collaborate with subject matter experts to translate business knowledge into reusable, AI-ready assets.

· Assess retrieval quality, answer groundedness, and system performance against measurable benchmarks.

Required Skills

· Expertise in information architecture, data modeling, taxonomy design, ontology modeling, and metadata strategy.

· Proficiency in knowledge graphs, semantic modeling, and technologies such as RDF, OWL, and SPARQL.

· Familiarity with RAG, Agentic RAG, GraphRAG, embeddings, vector databases, and semantic search techniques.

· Knowledge of chunking methodologies, retrieval optimization, reranking, and retrieval evaluation processes.

· Python programming skills, experience with APIs, data pipelines, and cloud-native engineering.

· Understanding of LLMs, agentic AI patterns, and AI evaluation methodologies.

· Strong analytical thinking, organized problem-solving capabilities, and effective stakeholder collaboration.

Experience & Qualifications

· Minimum of 7 years in data engineering, knowledge engineering, information architecture, search, cloud engineering, or a similar field.

· At least 3 years of experience in developing AI, LLM, RAG, knowledge graph, or enterprise search solutions.

· A Bachelor's or Master's degree in Computer Science, Data Science, Information Systems, or a related field, or equivalent practical experience.

Preferred Skills

· Familiarity with Azure AI Search, Azure OpenAI, Microsoft Fabric, Microsoft Graph, or the Copilot ecosystem.

· Experience with enterprise content platforms such as SharePoint, Microsoft 365, Confluence, ServiceNow, or similar.

· Exposure to agent frameworks, graph databases, Responsible AI principles, and knowledge governance practices.

Success Measures

· Enhanced retrieval relevance and high-quality grounded answers.

· Increased knowledge discoverability, reuse, and traceability.

· Scalable knowledge patterns effectively implemented across various AI use cases.