AI Data & Knowledge Engineer

What you'll do

• Design and develop data pipelines and knowledge layers for agentic AI applications.
• Build RAG-ready pipelines for document ingestion, parsing, metadata tagging, and vector search indexing.
• Design scalable architectures for semantic search, hybrid search, and knowledge graphs.
• Develop mechanisms for incremental synchronization of data to ensure real-time knowledge updates.
• Operate distributed data systems including SQL/NoSQL databases, vector search, and orchestration tools.
• Build pipelines to ingest and transform unstructured data like customer feedback and sales recordings.
• Develop data validation and monitoring processes to ensure quality and identify bias in AIML datasets.
• Implement data governance policies to ensure the ethical use of data in AI applications.

What we're looking for

• 5+ years of experience in designing, building, and maintaining scalable data solutions for large-scale analytics.
• Experience designing and building knowledge layers for AI systems, including knowledge graphs, RAG pipelines, and vector databases.
• Experience modeling enterprise knowledge and metadata within semantic layers to represent business entities and relationships.
• Proficiency in SQL and development experience with cloud database environments like Snowflake, Redshift, or Databricks.
• Proficiency in programming languages like Python, Java, and R, and open-source frameworks like Hadoop and Spark.
• Experience building data pipelines to ingest, transform, and continuously synchronize structured and unstructured enterprise data.
• Hands-on experience with modern cloud data stacks, including Git, CI/CD tools, Apache Airflow, and AWS, Azure, or Google Cloud.
• Experience architecting data pipelines through ETL tools and API integrations (preferred).