What are the responsibilities and job description for the AI Knowledge Engineer – Enterprise AI & Knowledge Management(Only W2) position at Nasscomm?
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