Data Engineer, AI Support

Insurance Institute for Business & Home Safety•Richburg, SC

About The Position

The Data Engineer, AI Support is a key technical partner in IBHS’s responsible adoption and application of artificial intelligence, machine learning, and advanced analytics. This position translates enterprise-wide needs across IBHS into practical, scalable data solutions that improve decision-making, operational effectiveness, and employee capabilities. The role combines Data Engineering expertise, solution development, technical consultation, and employee support. It works across business and technical teams to evaluate opportunities, develop and implement solutions, assess performance and risk, and help employees use approved AI-enabled tools effectively. This role strengthens IBHS’s enterprise-wide ability to use data and artificial intelligence thoughtfully, responsibly, and effectively. By connecting organizational priorities with technical capabilities, the Data Engineer, AI Support helps IBHS identify valuable use cases, improve access to actionable information, and build confidence in AI-enabled solutions. The position also helps establish consistent practices for solution quality, documentation, data handling, human review, and responsible AI use.

Requirements

Bachelor’s degree in data science, statistics, computer science, mathematics, engineering, or a related quantitative field.
Experience building ETL/data pipelines to clean, transform, integrate, and prepare structured, semi-structured, and unstructured data for AI/ML workflows.
Strong Python data manipulation skills, including efficient use of vectorized libraries for large-scale data processing, exploration, and visualization.
Working knowledge of SQL, NoSQL, data modeling, columnar formats, and modern data storage technologies.
Familiarity with preparing and versioning LLM/VLM training and evaluation datasets, including QA, preference/RL, multimodal, and human-annotated data.
Familiarity with embedding pipelines, vector databases, semantic search, RAG, and metadata-aware retrieval workflows.
Exposure to graph databases, knowledge graphs, and graph-based data modeling for AI applications.
Understanding of data quality, schema validation, dataset versioning, metadata, lineage, and reproducible train/validation/test splits with leakage prevention.
Familiarity with distributed data processing and workflow orchestration concepts such as DAGs, task dependencies, scheduling, and pipeline monitoring.
Comfortable working in Linux environments with Bash/shell scripting and basic automation.
Familiarity with experiment tracking and LLM observability tools such as Weights & Biases and Langfuse.
Basic understanding of PII handling, masking, hashing, tokenization, and de-identification within data pipelines.
Familiarity with CI/CD and infrastructure automation tools such as GitHub Actions, GitLab CI, and Terraform.
Comfortable working with research datasets that may be incomplete, inconsistent, or evolving, and able to investigate the data before implementing a solution.
Strong written communication, presentation, and technical-documentation skills.
Ability to build effective working relationships across business and technical functions.
Ability to exercise sound judgment, manage multiple priorities, and work independently while contributing to cross-functional initiatives.

Nice To Haves

Master’s degree in data science, statistics, computer science, artificial intelligence, machine learning, or a related field.
Experience supporting AI/ML research, scientific computing, or other data-intensive research environments.
Hands-on experience with cloud data platforms or services in Azure, AWS, or Google Cloud.
Experience with data orchestration and distributed processing tools such as Airflow, Prefect, Dagster, Spark, or similar technologies.
Familiarity with data annotation and human-in-the-loop platforms such as Label Studio or Prodigy, particularly for machine learning or multimodal datasets.

Responsibilities

Work closely with AI researchers and Data Engineering to prepare, organize, and improve the data used across research and machine learning projects.
Build reliable data workflows that move research data from raw sources into usable datasets for analysis, experimentation, training, and evaluation.
Explore and understand new datasets, identify quality issues or gaps, and help determine the best way to structure and use the data.
Support the preparation of datasets for a range of AI applications, including language, vision, multimodal, and retrieval-based systems.
Help ensure research datasets are consistent, traceable, reproducible, and well documented as they evolve over time.
Automate recurring data preparation and processing tasks to make research workflows more efficient and repeatable.
Develop clear summaries and visualizations that help the team understand datasets, patterns, and potential issues.
Support the integration of data across research tools, internal systems, and AI platforms.
Help protect sensitive information and follow appropriate data handling practices throughout the data lifecycle.
Contribute to an experimental research environment where datasets, methods, and requirements may change as projects develop.
Stay current on relevant developments in artificial intelligence, machine learning, data science, and emerging analytical technologies.