Lead Agentic Data Engineer

Job: Lead Agentic Data Engineer

Location : 1401 E Broad St Richmond, VA 23219

Duration: Long term

Resource will need to be in Richmond, VA quarterly. This position is slated to go through 12.31.2025.

Agentic Data Engineer to design, develop, and deploy data pipelines that leverage agentic AI that solve real-world problems.

We are seeking a highly skilled Agentic Data Engineer to design, develop, and deploy data pipelines that leverage agentic AI 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:

Guiding and mentoring AI engineers, helping them develop their skills and knowledge in the field.

Leading and managing AI projects, ensuring they stay on track, meet deadlines, and the findings are actionable and relevant.

Contributing to the creation and implementation of AI strategies that align with the organization's goals and objectives.

Designing and developing data pipelines for agentic systems, develop Robust data flows to handle complex interactions between AI agents and Data sources.

Ability to use advanced mathematical modeling, statistical analysis, and optimization techniques to gather and analyze data, identifying problems and developing solutions to improve efficiency in prompts.

Ability to train and fine tune large language models and Design and build the data architecture, including databases, data warehouses, 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 with high performance

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

Strong leadership, excellent problem-solving and communication skills

Proven experience in leading projects and teams, including the mentorship of AI engineers

The ability to engage in critical evaluation of information, hypothesis testing, and scenario analysis.

Flexibility in learning and adopting new technologies, methodologies, and tools to stay at the forefront of AI trends.

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.

Understanding the Big data Technologies

Required

1

Years

Experience developing ETL and ELT pipelines

Required

1

Years

Experience with Spark, GraphDB, Azure Databricks

Required

1

Years

Expertise in Data Partitioning

Required

1

Years

Experience with Data conflation

Required

3

Years

Experience developing Python Scripts

Required

3

Years

Experience training LLMs with structured and unstructured data sets

Required

2

Years

Experience with GIS spatial data

Required

---