Lead Agentic Integration Engineer

Role:Lead Agentic Integration Engineer

Duration:12months Location:Hybrid 1401 E Broad St Richmond, VA 23219

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.

Skills

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