Data Engineer – AI Platform (34664) - Edgar
Only for registered members Edgar, United States
18 hours ago
Data Engineer – AI Platform (AWS) · 10+ years building scalable ETL / ELT pipelines and enterprise data workflows. · 5–8+ years hands-on experience with AWS cloud data platforms (S3, Glue, Lambda, Bedrock, Redshift, etc.). · Strong experience ingesting and processing structured a ...
10+ years building scalable ETL / ELT pipelines and enterprise data workflows.
5–8+ years hands-on experience with AWS cloud data platforms (S3, Glue, Lambda, Bedrock, Redshift, etc.).
Strong experience ingesting and processing structured and unstructured data (documents, APIs, standards, object storage).
Experience designing data pipelines supporting AI / ML / GenAI production environments.
Practical knowledge of semantic search, embeddings, vector databases, and chunking strategies.
Experience enabling data for Retrieval Augmented Generation (RAG) architectures.
Hands-on expertise with Apache Airflow (or equivalent) for orchestration, monitoring, dependencies, and retries.
Experience designing model-ready datasets and supporting knowledge-based AI systems.
Strong understanding of data governance, metadata management, lineage, classification, and auditability.
Experience operating in Agile delivery environments supporting both rapid prototyping and production-grade pipelines.
Job description
Data Engineer – AI Platform (AWS)10+ years building scalable ETL / ELT pipelines and enterprise data workflows.
5–8+ years hands-on experience with AWS cloud data platforms (S3, Glue, Lambda, Bedrock, Redshift, etc.).
Strong experience ingesting and processing structured and unstructured data (documents, APIs, standards, object storage).
Experience designing data pipelines supporting AI / ML / GenAI production environments.
Practical knowledge of semantic search, embeddings, vector databases, and chunking strategies.
Experience enabling data for Retrieval Augmented Generation (RAG) architectures.
Hands-on expertise with Apache Airflow (or equivalent) for orchestration, monitoring, dependencies, and retries.
Experience designing model-ready datasets and supporting knowledge-based AI systems.
Strong understanding of data governance, metadata management, lineage, classification, and auditability.
Experience operating in Agile delivery environments supporting both rapid prototyping and production-grade pipelines.