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AI / ML Developer / Engineer
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Job Info
- Job Identification16850
- Posting Date07/10/2026, 11:13 AM
- Job RoleAI Engineer-Machine Learning
- Experience (In Years)3-6
- Job LocationIndia
Job Description
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 |
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