AI Data Engineer

Cinteot Inc. is a company focused on information technology, and they are seeking an AI Data Engineer to build and operate high-quality data pipelines for AI applications. The role involves preparing data for AI use cases, ensuring data quality and compliance, and collaborating with various teams to support AI development.

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

Requirements:

• 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
• Strong hands-on experience designing and operating data pipelines for analytics, ML, or AI workloads
• 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
• 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
• 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
• 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)
• Familiarity with governance and compliance frameworks relevant to healthcare (HIPAA, SOC 2, HITRUST) preferred