Apollo ITS
Role: Agentic Data Engineer
Location: Richmond, VA
Duration: 1+ month (extendable up to 12 months)
Resource will need to be in Richmond, VA quarterly. Position is expected to be needed through 12.31.2025
Job Description: Responsibilities:
Designing and developing data pipelines for agentic systems develop Robust data flows to handle complex interactions between AI agents and Data sources. Ability to train and fine tune large language models Design and build the data architecture, including databases, data lakes to support various data engineering tasks. Develop and manage Extract, Load, transform (ELT) processes to ensure data is accurately and efficiently moved from source systems to analytical platforms used in data science. Implement data pipelines that facilitate feedback loops, allowing human input to improve system performance in human-in-the-loop systems. Work with vector databases to store and retrieve embedding's efficiently. Collaborate with data scientists and engineers to preprocess data, train models, and integrate AI into applications. Optimize data storage and retrieval with high performance Statistical analysis, trends, patterns to create data formats from multiple sources. Qualifications:
Strong Data engineering fundamentals Utilize Big data frameworks like Spark/Databricks Training LLMs with structed and unstructured data sets. Understanding of Graph DB Experience with Azure Blob Storage, Azure Data Lakes, Azure Databricks Experience implementing Azure Machine Learning, Azure Computer Vision, Azure Video Indexer, Azure OpenAI models, Azure Media Services, Azure AI Search Determine effective data partitioning criteria Utilize data storage system spark to implement partition schemes Understanding core machine learning concepts and algorithms Familiarity with Cloud computing skills Strong programming skills in Python and experience with AI/ML frameworks. Proficiency in vector databases and embedding models for retrieval tasks. Expertise in integrating with AI agent frameworks. Experience with cloud AI services (Azure AI). Experience with GIS spatial data to create markers on maps ( lat long nearest topology of road, geo-locate between datasets, correlation etc.). Experience with Department of Transportation Data Domains developing an AI Composite Agentic Solution designed to identify and analyze data models, connect & correlate information to validate hypotheses, forecast, predict and recommend potential strategies and conduct What-if analysis. Bachelor's or master's degree in computer science, AI, Data Science, or a related field. Required/Desired Skills:
Understanding the Big data Technologies Experience developing ETL and ELT pipelines Experience with Spark, GraphDB, Azure Databricks Expertise in Data Partitioning Experience with Data conflation Experience developing Python Scripts Experience training LLMs with structured and unstructured datasets Experience with GIS spatial data
Resource will need to be in Richmond, VA quarterly. Position is expected to be needed through 12.31.2025
Job Description: Responsibilities:
Designing and developing data pipelines for agentic systems develop Robust data flows to handle complex interactions between AI agents and Data sources. Ability to train and fine tune large language models Design and build the data architecture, including databases, data lakes to support various data engineering tasks. Develop and manage Extract, Load, transform (ELT) processes to ensure data is accurately and efficiently moved from source systems to analytical platforms used in data science. Implement data pipelines that facilitate feedback loops, allowing human input to improve system performance in human-in-the-loop systems. Work with vector databases to store and retrieve embedding's efficiently. Collaborate with data scientists and engineers to preprocess data, train models, and integrate AI into applications. Optimize data storage and retrieval with high performance Statistical analysis, trends, patterns to create data formats from multiple sources. Qualifications:
Strong Data engineering fundamentals Utilize Big data frameworks like Spark/Databricks Training LLMs with structed and unstructured data sets. Understanding of Graph DB Experience with Azure Blob Storage, Azure Data Lakes, Azure Databricks Experience implementing Azure Machine Learning, Azure Computer Vision, Azure Video Indexer, Azure OpenAI models, Azure Media Services, Azure AI Search Determine effective data partitioning criteria Utilize data storage system spark to implement partition schemes Understanding core machine learning concepts and algorithms Familiarity with Cloud computing skills Strong programming skills in Python and experience with AI/ML frameworks. Proficiency in vector databases and embedding models for retrieval tasks. Expertise in integrating with AI agent frameworks. Experience with cloud AI services (Azure AI). Experience with GIS spatial data to create markers on maps ( lat long nearest topology of road, geo-locate between datasets, correlation etc.). Experience with Department of Transportation Data Domains developing an AI Composite Agentic Solution designed to identify and analyze data models, connect & correlate information to validate hypotheses, forecast, predict and recommend potential strategies and conduct What-if analysis. Bachelor's or master's degree in computer science, AI, Data Science, or a related field. Required/Desired Skills:
Understanding the Big data Technologies Experience developing ETL and ELT pipelines Experience with Spark, GraphDB, Azure Databricks Expertise in Data Partitioning Experience with Data conflation Experience developing Python Scripts Experience training LLMs with structured and unstructured datasets Experience with GIS spatial data