AI/ML Engineer

AI/ML Engineer

Onsite in Dallas, TX + Charlotte, NC

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

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.

• 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)

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.

• 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

18.00 Years of Experience