What are the responsibilities and job description for the AI/ML Engineer - Knowledge Graph position at SANS Consulting Services, Inc.?
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 Graphs
Milestone 1 - Enhance the monitoring target state platform to perform AI based Quality Analysis / Quality Control on Issue Intake requests
Description:
- Leverage the existing monitoring target state platform to perform AI‐based quality analysis and quality control on BCM Issue Intake requests
- Apply standardized orchestration, prompt management, observability, and governance to improve consistency, accuracy, and auditability of intake quality assessments
Deliverables:
- Issue Intake QA/QC Workflows built using the existing orchestration and scheduling capabilities of the monitoring platform
- Quality Evaluation Prompts leveraging established prompt templates, prompt chaining, and prompt versioning for intake quality checks
- Intake Data Ingestion & Processing utilizing existing data connectors, storage, and processing patterns for unstructured request content
- QA/QC Execution Observability reusing platform logging, metrics, run status, error handling, retries, and audit trails
- Quality Scores & Outputs producing mathematical quality indicators and consumable results for BCM review and downstream reporting
- Documentation & BCM Enablement including intake QA/QC logic, operating guidance, and alignment to BCM control processes
Milestone 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 Intake
- Leverage the monitoring platform’s AI orchestration, prompt management, observability, and governance capabilities to power intelligent context enrichment and decision support
Deliverables:
- AI‐Driven Knowledge Graph Model representing risks, issues, controls, policies, and relationships with semantic and contextual enrichment
- AI‐Based Entity Extraction & Linking leveraging GenAI to identify, classify, and relate entities from unstructured Issue Intake content
- Contextual AI Reasoning for Issue Intake enabling real‐time recommendations, relationship discovery, and impact analysis using KG‐augmented prompts
- KG‐Augmented 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.