Employer description & source details
Design and build scalable ETL/ELT and AI pipelines using Databricks, PySpark, Delta Lake and SQL. Develop and maintain Lakehouse architectures using Bronze, Silver and Gold data layers. Build and optimise Delta Live Tables, Databricks Workflows and job orchestration processes. Develop and deploy production-grade Generative AI and agent-based solutions. Create and maintain RAG pipelines and AI agent frameworks. Support MLOps activities, including model training and inference pipelines using MLflow. Implement data quality monitoring, governance controls and metadata management through Unity Catalog. Develop event-driven integrations using Azure cloud services. Collaborate with cross-functional teams to create feature stores and AI training datasets. Integrate Databricks with wider Azure services including ADLS Gen2, Azure Data Factory, Azure Key Vault and Azure DevOps. Contribute to the development of data engineering best practices and centre of excellence initiatives. Mentor and support engineering teams in AI technologies and capabilities. Strong experience as a Data Engineer, AI Engineer, Machine Learning Engineer, or similar role. Hands-on experience building and deploying production-grade: Retrieval Augmented Generation (RAG) solutions Data pipeline solutions within Databricks Strong programming skills in Python and SQL. Experience with Apache Spark and distributed data processing. Experience implementing Generative AI solutions within Databricks Workflows and Jobs. Strong knowledge of Databricks Lakehouse architecture. Experience implementing event-driven integrations using: Understanding of data modelling and Medallion/Lakehouse architecture. Experience with Git-based version control and CI/CD practices. Strong understanding of AI/ML pipelines, including data prep
Extracted skills: python, sql, aws, azure, machine learning