AI Data Engineer

About the role

The AI Data Engineer is responsible for building and operating high-quality, governed, and AI-ready data pipelines that power enterprise GenAI and agent-based use cases. This role focuses on preparing data for retrieval-augmented generation (RAG), managing embeddings and vector indexes, and ensuring data quality, lineage, and compliance across the AI platform.

Major Responsibilities

• Design, build, and maintain data pipelines that ingest, transform, and curate structured and unstructured data for AI use cases.
• Prepare RAG-ready datasets by applying metadata enrichment, chunking, normalization, and document parsing patterns aligned to platform standards.
• Partner with source system teams and domain SMEs to understand data semantics and ensure accurate representation for AI consumption.
• Create and maintain embedding pipelines, including generation, refresh, and lifecycle management.
• Own vector index maintenance, including re-indexing strategies, performance tuning, and cleanup of stale or unused embeddings.
• Support knowledge grounding for AI agents by ensuring source attribution, consistency, and traceability.
• Implement data quality checks, validation rules, and monitoring to ensure accuracy, completeness, and reliability of AI datasets.
• Ensure all AI data pipelines comply with enterprise data governance, privacy, and information management policies, including support for regulated and sensitive data use cases.
• Collaborate with Architecture, Security, and Information Governance partners to align data handling with approved AI patterns and risk controls.
• Support AI Engineers during onboarding and troubleshooting by diagnosing data issues that affect agent behavior or retrieval accuracy.
• Contribute reusable data patterns, templates, and documentation to accelerate future AI use cases.
• Participate in platform support activities defined in the AI CoE RACI, particularly those related to data grounding and vector maintenance.
• Optimize data and embedding pipelines for performance, scalability, and cost efficiency, in partnership with Platform Engineers.
• Monitor data freshness and usage trends to recommend retirement, refresh, or enhancement of datasets supporting AI agents.

Qualifications

• Bachelor’s degree in computer science, Engineering, Data Science, or a related technical discipline OR equivalent combination of education and relevant experience.
• Demonstrated experience designing and operating production-grade data pipelines in an enterprise environment.
• Experience working with unstructured data (documents, text, PDFs) and preparing data for analytics, ML, or AI use cases.
• Working knowledge of embeddings, vector databases, and retrieval patterns used in modern AI and GenAI solutions.
• Strong understanding of data quality, lineage, and governance concepts.

Additional Licensing, Certifications, Registrations

• Professional certification(s) in area of expertise a plus
• AWS Machine Learning Specialty, Azure AI Engineer Associate, or equivalent cloud certifications.
• Databricks and/or Snowflake certifications

Knowledge, Skills, and Abilities

• Strong hands-on experience designing and operating data pipelines for analytics, ML, or AI workloads.
• Experience working with unstructured data (documents, PDFs, text) and preparing it for downstream AI or search use cases.
• Knowledge of embeddings, vector databases, and retrieval patterns used in RAG or knowledge-based AI systems.
• Strong understanding of data quality, lineage, and governance concepts in enterprise environments.
• Experience supporting GenAI or agentic AI platforms in a regulated enterprise environment (e.g., healthcare, financial services).
• Familiarity with cloud-native data services and AI platforms commonly used for enterprise AI enablement.
• Experience partnering with platform and application teams in a federated or hub-and-spoke operating model.
• Understanding of healthcare compliance standards (HIPAA, HITRUST) and ethical AI practices (bias, explainability, data privacy).
• Ability to collaborate effectively with cross-functional teams and translate business requirements into technical solutions.
• Strong problem-solving and innovation mindset, with the ability to adapt generative AI to real-world challenges in healthcare and ability to adapt to and adapt to evolving priorities and technologies.
• Familiarity with governance and compliance frameworks relevant to healthcare (HIPAA, SOC 2, HITRUST) preferred

Benefits

• Complete Insurance Coverage
• Blue Cross Medical, Delta Dental, Vision, Life
• 401k with Company Contribution
• Tuition Reimbursement
• Generous Paid Time Off (including your birthday!)

Preferred Skills

• Data pipelines