Key Responsibilities:
- Configure and fine-tune AI agents (Data Governance, Data Quality, Lineage, Stewardship) on the EXLdata.ai™ platform
- Apply prompt engineering techniques to optimize agent behavior, accuracy, and output quality
- Design and automate governance workflows and data stewardship processes using AI agent orchestration
- Perform current-state analysis and document metadata, data lineage, and governance processes
- Support configuration of governance workflows and reporting dashboards for stewards and executives
- Integrate AI agents with backend systems, Knowledge Graph (Neo4j), and Vector Database (Milvus)
- Collaborate with GCP Engineers, Solution Architects, and Data Analysts to deliver sprint objectives
- Participate in Agile ceremonies — daily standups, sprint demos, and retrospectives
- Troubleshoot agent performance, data pipeline issues, and workflow errors in the GKE environment
Qualifications:
- 3+ years of experience in AI/ML development, agent configuration, or LLM-based application development
- Strong expertise in prompt engineering and AI workflow automation
- Hands-on experience with AI agent frameworks and orchestration tools
- Knowledge of data governance concepts: data quality, stewardship, lineage, MDM/RDM, and metadata management
- Familiarity with governance agents: Data Quality (DQ), Stewardship, MDM/RDM agents
- Experience working with REST APIs and event-driven integration
- Proficiency in Python for scripting, automation, and data processing
- Experience with CI/CD pipelines using GitHub / GitHub Actions
- Strong analytical skills to document and assess current-state data and governance processes
Preferred Skills
- Experience in the insurance domain (Claims, Underwriting, or Policy data)
- Familiarity with EXLdata.ai™ platform or similar agentic data intelligence platforms
- GCP Professional certification (ML Engineer, Data Engineer, or Cloud Developer)
- Experience with offshore/onshore hybrid Agile delivery models
Skills & Experience
The following tools are part of the EXLdata.ai™ GCP architecture. Familiarity is an advantage — not all are mandatory. Training and ramp-up support will be provided.
Must-Have Skills & Experience
EXLdata.ai™ Agents
Data Governance, Data Quality, Data Lineage, Stewardship Workbench agents
Vertex AI (GCP)
Google AI/ML platform for model serving and agent inference
BigQuery
Managed analytics data warehouse for querying governance data
GitHub / Git + Actions
Source control and CI/CD for agent configuration and deployment
Python
Primary language for agent scripting and workflow automation
Nginx / Orchestrator
API gateway and agent orchestration layer within GKE
Preferred — Nice to Have
GKE (Google Kubernetes Engine)
Container orchestration platform hosting EXLdata.ai™ agents
Neo4j Graph Database
Knowledge graph for entity relationships and data lineage
Milvus Vector Database
Vector DB for semantic search and embedding storage (open-source)
Cloud SQL
Managed relational DB for metadata storage
Google Secret Manager (CSI)
Secrets management via CSI Secret Store integration in GKE
Google Filestore
Persistent shared storage (RWX, CSI-backed PVC)
Guidewire APIs / Events
Insurance platform integration for Claims & Underwriting data
Okta
Identity access management and access scoping via VPC rules
Awareness Level — Environment Context
Cloud Logging / gCloud CLI
Operational logging and CLI access for environment support
IAM (Identity & Access Mgmt)
GCP role-based access control and service account management
Cloud KMS / Secrets Manager
Key management and secret storage for secure deployments
Artifact Registry
Container image registry for agent Docker images
Cloud DNS
DNS routing for subdomain-based service access
Backup & DR Service
Disaster recovery and backup for platform resilience
Responsibilities
Key Responsibilities:
- Configure and fine-tune AI agents (Data Governance, Data Quality, Lineage, Stewardship) on the EXLdata.ai™ platform
- Apply prompt engineering techniques to optimize agent behavior, accuracy, and output quality
- Design and automate governance workflows and data stewardship processes using AI agent orchestration
- Perform current-state analysis and document metadata, data lineage, and governance processes
- Support configuration of governance workflows and reporting dashboards for stewards and executives
- Integrate AI agents with backend systems, Knowledge Graph (Neo4j), and Vector Database (Milvus)
- Collaborate with GCP Engineers, Solution Architects, and Data Analysts to deliver sprint objectives
- Participate in Agile ceremonies — daily standups, sprint demos, and retrospectives
- Troubleshoot agent performance, data pipeline issues, and workflow errors in the GKE environment
Qualifications:
- 3+ years of experience in AI/ML development, agent configuration, or LLM-based application development
- Strong expertise in prompt engineering and AI workflow automation
- Hands-on experience with AI agent frameworks and orchestration tools
- Knowledge of data governance concepts: data quality, stewardship, lineage, MDM/RDM, and metadata management
- Familiarity with governance agents: Data Quality (DQ), Stewardship, MDM/RDM agents
- Experience working with REST APIs and event-driven integration
- Proficiency in Python for scripting, automation, and data processing
- Experience with CI/CD pipelines using GitHub / GitHub Actions
- Strong analytical skills to document and assess current-state data and governance processes
Preferred Skills
- Experience in the insurance domain (Claims, Underwriting, or Policy data)
- Familiarity with EXLdata.ai™ platform or similar agentic data intelligence platforms
- GCP Professional certification (ML Engineer, Data Engineer, or Cloud Developer)
- Experience with offshore/onshore hybrid Agile delivery models
Skills & Experience
The following tools are part of the EXLdata.ai™ GCP architecture. Familiarity is an advantage — not all are mandatory. Training and ramp-up support will be provided.
Must-Have Skills & Experience
EXLdata.ai™ Agents
Data Governance, Data Quality, Data Lineage, Stewardship Workbench agents
Vertex AI (GCP)
Google AI/ML platform for model serving and agent inference
BigQuery
Managed analytics data warehouse for querying governance data
GitHub / Git + Actions
Source control and CI/CD for agent configuration and deployment
Python
Primary language for agent scripting and workflow automation
Nginx / Orchestrator
API gateway and agent orchestration layer within GKE
Preferred — Nice to Have
GKE (Google Kubernetes Engine)
Container orchestration platform hosting EXLdata.ai™ agents
Neo4j Graph Database
Knowledge graph for entity relationships and data lineage
Milvus Vector Database
Vector DB for semantic search and embedding storage (open-source)
Cloud SQL
Managed relational DB for metadata storage
Google Secret Manager (CSI)
Secrets management via CSI Secret Store integration in GKE
Google Filestore
Persistent shared storage (RWX, CSI-backed PVC)
Guidewire APIs / Events
Insurance platform integration for Claims & Underwriting data
Okta
Identity access management and access scoping via VPC rules
Awareness Level — Environment Context
Cloud Logging / gCloud CLI
Operational logging and CLI access for environment support
IAM (Identity & Access Mgmt)
GCP role-based access control and service account management
Cloud KMS / Secrets Manager
Key management and secret storage for secure deployments
Artifact Registry
Container image registry for agent Docker images
Cloud DNS
DNS routing for subdomain-based service access
Backup & DR Service
Disaster recovery and backup for platform resilience
Qualifications
Key Responsibilities:
- Configure and fine-tune AI agents (Data Governance, Data Quality, Lineage, Stewardship) on the EXLdata.ai™ platform
- Apply prompt engineering techniques to optimize agent behavior, accuracy, and output quality
- Design and automate governance workflows and data stewardship processes using AI agent orchestration
- Perform current-state analysis and document metadata, data lineage, and governance processes
- Support configuration of governance workflows and reporting dashboards for stewards and executives
- Integrate AI agents with backend systems, Knowledge Graph (Neo4j), and Vector Database (Milvus)
- Collaborate with GCP Engineers, Solution Architects, and Data Analysts to deliver sprint objectives
- Participate in Agile ceremonies — daily standups, sprint demos, and retrospectives
- Troubleshoot agent performance, data pipeline issues, and workflow errors in the GKE environment
Qualifications:
- 3+ years of experience in AI/ML development, agent configuration, or LLM-based application development
- Strong expertise in prompt engineering and AI workflow automation
- Hands-on experience with AI agent frameworks and orchestration tools
- Knowledge of data governance concepts: data quality, stewardship, lineage, MDM/RDM, and metadata management
- Familiarity with governance agents: Data Quality (DQ), Stewardship, MDM/RDM agents
- Experience working with REST APIs and event-driven integration
- Proficiency in Python for scripting, automation, and data processing
- Experience with CI/CD pipelines using GitHub / GitHub Actions
- Strong analytical skills to document and assess current-state data and governance processes
Preferred Skills
- Experience in the insurance domain (Claims, Underwriting, or Policy data)
- Familiarity with EXLdata.ai™ platform or similar agentic data intelligence platforms
- GCP Professional certification (ML Engineer, Data Engineer, or Cloud Developer)
- Experience with offshore/onshore hybrid Agile delivery models
Skills & Experience
The following tools are part of the EXLdata.ai™ GCP architecture. Familiarity is an advantage — not all are mandatory. Training and ramp-up support will be provided.
Must-Have Skills & Experience
EXLdata.ai™ Agents
Data Governance, Data Quality, Data Lineage, Stewardship Workbench agents
Vertex AI (GCP)
Google AI/ML platform for model serving and agent inference
BigQuery
Managed analytics data warehouse for querying governance data
GitHub / Git + Actions
Source control and CI/CD for agent configuration and deployment
Python
Primary language for agent scripting and workflow automation
Nginx / Orchestrator
API gateway and agent orchestration layer within GKE
Preferred — Nice to Have
GKE (Google Kubernetes Engine)
Container orchestration platform hosting EXLdata.ai™ agents
Neo4j Graph Database
Knowledge graph for entity relationships and data lineage
Milvus Vector Database
Vector DB for semantic search and embedding storage (open-source)
Cloud SQL
Managed relational DB for metadata storage
Google Secret Manager (CSI)
Secrets management via CSI Secret Store integration in GKE
Google Filestore
Persistent shared storage (RWX, CSI-backed PVC)
Guidewire APIs / Events
Insurance platform integration for Claims & Underwriting data
Okta
Identity access management and access scoping via VPC rules
Awareness Level — Environment Context
Cloud Logging / gCloud CLI
Operational logging and CLI access for environment support
IAM (Identity & Access Mgmt)
GCP role-based access control and service account management
Cloud KMS / Secrets Manager
Key management and secret storage for secure deployments
Artifact Registry
Container image registry for agent Docker images
Cloud DNS
DNS routing for subdomain-based service access
Backup & DR Service
Disaster recovery and backup for platform resilience
