AI / ML Developer / Engineer

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 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 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