Generative AI Engineer

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Build a knowledge graph capability allowing BCMs to reference associated risks, issues, controls etc during Issue Intake, (plus other potential KG use cases)

Description:

• Build an AIdriven knowledge graph capability that enables BCMs to automatically Client, reason over, and reference related risks, issues, controls, and policies during Issue Intake
• Leverage the monitoring platform s AI orchestration, prompt management, observability, and governance capabilities to power intelligent context enrichment and decision support

Deliverables:

• AIDriven Knowledge Graph Model representing risks, issues, controls, policies, and relationships with semantic and contextual enrichment
• AIBased Entity Extraction & Linking leveraging GenAI to identify, classify, and relate entities from unstructured Issue Intake content
• Contextual AI Reasoning for Issue Intake enabling realtime recommendations, relationship discovery, and impact analysis using KGaugmented prompts
• KGAugmented Prompt Framework reusing existing prompt templates, prompt chaining, and prompt versioning to incorporate knowledge graph context
• Orchestrated AI Workflows leveraging existing scheduling, execution controls, and observability for KG population and inference

Governance, Audit & Observability capturing AI decisions, entity relationships, prompt versions, and lineage for BCM compliance and control assurance.

Overall we need:

Experienced AI/ML Engineer with a strong foundation in knowledge graph engineering and generative AI, Agentic AI to design, build, and scale intelligent data pipelines that transform largescale unstructured data into enterprisegrade Knowledge Graphs