Job ID: VA-767067 (914290714)
Hybrid/Local Agentic/Data Engineer (15+) with ETL, ELT, Spark, AI/ML, GraphDB, Azure Databricks, LLMs, Data conflation and GIS experience
Location: Richmond, VA (VDOT)
Duration: 12 Months
Interview: Both Phone and In Person
Resources will need to be in Richmond, VA quarterly.
Skills:
Understanding the Big data technologies Required 5 Years
Experience developing ETL and ELT pipelines Required 5 Years
Experience with Spark, GraphDB, Azure Databricks Required 5 Years
Experience training LLMs with structured and unstructured data sets Required 4 Years
Experience in Data Partitioning and Data conflation Required 3 Years
Experience with GIS spatial data Required 3 Years
The Virginia Department of Transportation’s Information Technology Division is seeking a highly skilled Agentic Engineer to design, develop that solve real-world problems. The ideal candidate will have experience in designing data process to support agentic systems. ensure data quality and facilitating interaction between agents and data.
Responsibilities
Design and develop data pipelines for agentic systems; develop robust data flows to handle complex interactions between AI agents and data sources.
Train and fine-tune large language models (LLMs).
Design and build data architecture, including databases and 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 embeddings efficiently.
Collaborate with data scientists and engineers to preprocess data, train models, and integrate AI into applications.
Optimize data storage and retrieval for high performance.
Conduct statistical analysis to identify trends and patterns and create data formats from multiple sources.
Qualifications:
Strong data engineering fundamentals.
Experience with big data frameworks such as Apache Spark and Azure Databricks.
Ability to train LLMs using structured and unstructured datasets.
Understanding of graph databases.
Experience with Azure services including Blob Storage, Data Lakes, Databricks, Azure Machine Learning, Azure Computer Vision, Azure Video Indexer, Azure OpenAI, Azure Media Services, and Azure AI Search.
Proficient in determining effective data partitioning criteria and implementing partition schemas using Spark.
Understanding of core machine learning concepts and algorithms.
Familiarity with cloud computing concepts and practices.
Strong programming skills in Python and experience with AI/ML frameworks.
Proficiency in working with vector databases and embedding models for retrieval tasks.
Expertise in integrating with AI agent frameworks.
Experience with cloud-based AI services, especially Azure AI.
Proficient with version control systems such as Git.
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.