AI Engineer

• Design and develop scalable data pipelines, backend services, and APIs.
• Build knowledge graph solutions, including graph data modeling, relationship mapping, and graph traversal logic.
• Build ETL pipelines.
• Design data models.
• Implement GraphRAG solutions combining knowledge graphs, vector search, metadata, and LLM-based retrieval.
• Create Source to Target Mappings (STMs) for ETL specifications.
• Implement automated ingestion patterns, incremental (delta) updates, and streaming/CDC workflows.
• Gather requirements, set targets, define interface specifications, and conduct design sessions.
• Work closely with data consumers to ensure proper integration.
• Adapt and learn in a fast-paced project environment.

Senior AI Engineer, GraphRAG, Neo4J

• Build and maintain scalable data pipelines using Databricks and Azure Data Lake Storage Gen2 (ADLS Gen2), ensuring reliability and performance across all pipeline stages.
• Implement the Medallion Architecture (Raw → Silver → Gold layers) to clean, normalise, and structure technical documentation for downstream AI consumption.
• Design and develop the Reasoning Layer using frameworks such as LangChain or LlamaIndex to manage LLM logic, prompt routing, and tool orchestration.
• Manage and tune Vector Databases (e.g. Pinecone, Weaviate, or Azure AI Search) to enable fast and accurate semantic retrieval across the corporate knowledge base.
• Develop Azure Apps and Azure Functions to connect the AI system to external enterprise platforms such as Salesforce and SharePoint, enabling end-to-end automation.

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Senior AI Engineer, GraphRAG, Neo4J

Data & AI Engineer