Job Description:
- As an AI Data Architect you will own the data pipeline that powers our agentic AI platform
- You will design the ingestion transformation contextualization enrichment validation and semantic modelling layers that connect a wide range of structured and unstructured enterprise data sources into an AI ready data corpus
- This is a senior individual contributor role with real ownership over the data foundation of the venture
- The role requires hands on experience in enterprise data engineering schema discovery data profiling data quality automation ontology implementation knowledge graph integration and cloud scale pipeline engineering
Key Responsibilities:
- Design production grade enterprise connectors and ETL ELT pipelines for both structured enterprise systems such as ERP CRM OSS BSS billing finance HR and unstructured sources such as emails documents logs transcripts and media files
- Build ingestion and transformation pipelines using Python SQL PySpark Apache Spark Airflow dbt Dagster Flink or equivalent technologies
- Create frameworks for data labelling contextualization harmonization enrichment and classification workflows to configure AI agents
- Architect integration with knowledge graphs and vector databases for hybrid search semantic retrieval contextual reasoning and AI ready data access
- Build and maintain Ontology knowledge graph pipelines using Neo4j RDF OWL Apache Jena Stardog GraphDB or equivalent technologies
- Implement graph validation frameworks such as SHACL or ShEx to programmatically enforce data integrity rules over enterprise knowledge graphs
- Implement data quality automation using frameworks such as Great Expectations AWS Glue DataBrew dbt tests custom validation pipelines or equivalent tools
Technical Requirements:
- Strong production experience on modern data platforms such as Databricks Snowflake BigQuery cloud data lakes lakehouses or equivalent enterprise data platforms
- Deep working knowledge of Python SQL PySpark Apache Spark and modern data pipeline development practices
- Hands on experience with both structured and unstructured data ingestion at enterprise scale
- Strong experience in building pipelines for enterprise sources such as ERP CRM OSS BSS billing systems finance systems ServiceNow Salesforce SAP Oracle and legacy databases
- Working knowledge of vector databases such as Pinecone Weaviate pgvector Milvus Chroma or equivalent technologies
- Hands on knowledge of knowledge graphs graph data modelling graph querying and enterprise graph implementation using Neo4j Cypher RDF OWL or equivalent technologies
Additional Responsibilities:
- Exposure to telecom BFSI manufacturing or other complex enterprise domains
- Experience with OSS BSS ERP CRM billing order management product catalog service inventory or network inventory systems
- Experience with RDF triple stores such as Apache Jena Stardog GraphDB Amazon Neptune or equivalent technologies
- Experience with data catalogues metadata management tools lineage platforms or governance platforms
Preferred Skills:
Technology->AI-Data science->Databricks Machine Learning,Technology->Data Management->Data Architecture->Data Architecture - Data Management,Technology->Data Management - Data Integration->Informatica->Informatica - Data Explorer,Technology->Data on Cloud-DataStore->Snowflake,Technology->Microsoft Technologies->Microsoft Technologies- ALL