Agentic Data Engineer to design, develop, and deploy data pipelines that leverage agentic AI that solve real-world problems.
The Client is 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.
| Skill | Required / Desired | Amount | of Experience |
| 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 | 3 | Years |