AI/ML Engineer - Knowledge Graph

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 large-scale unstructured data into enterprise-grade Knowledge GraphsMilestone 1 - Enhance the monitoring target state platform to perform AI based Quality Analysis / Quality Control on Issue Intake requestsDescription:Leverage the existing monitoring target state platform to perform AI-based quality analysis and quality control on BCM Issue Intake requestsApply standardized orchestration, prompt management, observability, and governance to improve consistency, accuracy, and auditability of intake quality assessmentsDeliverables:Issue Intake QA/QC Workflows built using the existing orchestration and scheduling capabilities of the monitoring platformQuality Evaluation Prompts leveraging established prompt templates, prompt chaining, and prompt versioning for intake quality checksIntake Data Ingestion & Processing utilizing existing data connectors, storage, and processing patterns for unstructured request contentQA/QC Execution Observability reusing platform logging, metrics, run status, error handling, retries, and audit trailsQuality Scores & Outputs producing mathematical quality indicators and consumable results for BCM review and downstream reportingDocumentation & BCM Enablement including intake QA/QC logic, operating guidance, and alignment to BCM control processesMilestone 2 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 AI-driven knowledge graph capability that enables BCMs to automatically Client, reason over, and reference related risks, issues, controls, and policies during Issue IntakeLeverage the monitoring platform's AI orchestration, prompt management, observability, and governance capabilities to power intelligent context enrichment and decision supportDeliverables:AI-Driven Knowledge Graph Model representing risks, issues, controls, policies, and relationships with semantic and contextual enrichmentAI-Based Entity Extraction & Linking leveraging GenAI to identify, classify, and relate entities from unstructured Issue Intake contentContextual AI Reasoning for Issue Intake enabling real-time recommendations, relationship discovery, and impact analysis using KG-augmented promptsKG-Augmented Prompt Framework reusing existing prompt templates, prompt chaining, and prompt versioning to incorporate knowledge graph contextOrchestrated AI Workflows leveraging existing scheduling, execution controls, and observability for KG population and inferenceGovernance, Audit & Observability capturing AI decisions, entity relationships, prompt versions, and lineage for BCM compliance and control assurance.

Created: 2026-05-09