AI Data Engineer

Job Overview:

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. As part of the AI CoE Technology pod, the AI Data Engineer enables rapid, responsible AI development by delivering reusable, scalable data foundations. This is a hands‑on individual contributor role within the AI CoE – Technology pod, working closely with AI Platform Engineers and AI Engineers to support both shared platform capabilities and priority AI use cases. The role is intentionally centralized to avoid fragmented data pipelines and to ensure consistent governance, quality, and reuse across the enterprise.

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