AI/ML Knowledge Engineer - Charlotte - NC(Only W2)

Role Summary We are seeking AI Knowledge Engineer to join an innovative and high-impact team building AI capabilities within enterprise systems for a leading wealth management client. You will design, structure, and maintain the knowledge framework and content that power AI assistants, AI search, and decision-support applications. This role focuses on turning existing and new enterprise content into usable, trusted, and scalable knowledge assets that improve retrieval, answer quality, and user experience. This is an opportunity to shape the next generation of enterprise AI in a highly visible environment, where innovation, rigor, and business value all matter. The ideal candidate is curious, hands-on, and energized by solving real-world problems at the intersection of AI, enterprise technology, and wealth management Key Responsibilities: Build and maintain knowledge framework such as schemas, taxonomies, ontologies, and metadata structures Curate, normalize, and classify enterprise content from diverse sources Design content pipelines for ingestion, enrichment, deduplication, and versioning Partner with data, product, and AI engineers to improve search, retrieval, and grounding quality Define governance standards for content quality, freshness, lineage, and access control Evaluate knowledge gaps and recommend improvements to AI response accuracy Support prompt, retrieval, and indexing strategies for LLM-based applications Monitor system performance and content feedback loops to continuously improve results Required Qualifications Bachelor’s degree in Computer Science, Information Science, Linguistics, Knowledge Management, or related field 6+ years’ experience in knowledge engineering, enterprise search, semantic information architecture, content operations, or related technical roles Experience with knowledge graphs, taxonomy design, metadata management, or semantic search Familiarity with AI/ML concepts, retrieval-augmented generation, and enterprise information architecture Strong analytical skills and comfort working across technical and business teams Experience with content operations, data governance, or knowledge management tools Preferred Qualifications Experience with vector databases, graph databases, or enterprise search platforms Exposure to LLM evaluation, prompt engineering, or semantic layer design Familiarity with Python, SQL, APIs, or automation for content workflows Experience in large enterprise environments, consulting or professional services Structured thinking and information modeling Cross-functional collaboration Attention to detail and quality Problem solving in ambiguous environments Communication with technical and non-technical stakeholders