Client 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.

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.

SKILL REQUIRED / DESIRED AMOUNT OF EXPERIENCE

• 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
  Job Type: Contract
  Pay: $70.00 - $80.00 per hour
  Expected hours: 40 per week

Schedule:

• 8 hour shift

Work Location: Hybrid remote in Richmond, VA 2321

• 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.