What are the responsibilities and job description for the AI/ML + Knowledge Graph GenAI Engineer position at Saransh Inc?
Job Title: AI/ML Knowledge Graph GenAI Engineer
Location: Dallas, TX & Charlotte, NC (Onsite from Day 1)
Job Type: Contract
AIML Knowledge Graph GenAI Engineer (8 years)
Skill Metrics:
Data Management
Clover ETL
Yes
1
Job Description:
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 discover, 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.
Location: Dallas, TX & Charlotte, NC (Onsite from Day 1)
Job Type: Contract
AIML Knowledge Graph GenAI Engineer (8 years)
Skill Metrics:
Data Management
Clover ETL
Yes
1
Job Description:
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 discover, 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.