AI Data & Knowledge Engineer

What you'll do

• Design and develop data pipelines and knowledge layers for agentic AI applications.
• Build RAG-ready pipelines including document ingestion, parsing, metadata tagging, and vector indexing.
• Create scalable architecture for semantic search, hybrid search, and knowledge graphs to support text-to-SQL generation.
• Develop systems for the continuous synchronization of data to ensure real-time accuracy in AI responses.
• Build and maintain infrastructure for processing both structured and unstructured data from diverse sources.
• Implement data quality monitoring, validation processes, and bias detection for AIML datasets.
• Translate business requirements into technical data models and actionable knowledge layers for internal teams.
• Establish data governance policies to ensure the ethical use of information in AI applications.

What we're looking for

• Experience designing and building knowledge layers, RAG pipelines, and vector databases for LLM-driven applications.
• Experience modeling enterprise knowledge and metadata within semantic layers to represent business entities and relationships.
• 5+ years of experience in designing, building, and maintaining scalable data solutions for large-scale analytics.
• Proficiency in SQL and development experience with cloud database environments like Snowflake, Redshift, or Databricks.
• Proficiency in programming languages such as Python, Java, and R, along with frameworks like Hadoop and Spark.
• Experience building pipelines to ingest, transform, and synchronize structured and unstructured enterprise data from multiple sources.
• Hands-on experience with modern cloud data stacks including Git, CI/CD tools, Apache Airflow, and monitoring systems.
• Experience with major cloud platforms such as AWS, Azure, or Google Cloud.