Note: The job is a remote job and is open to candidates in USA. 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
Skills
- 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
Benefits
- Health Insurance
- Dental Insurance
- Vision Insurance
- 401(k) Matching
- Paid Time Off (PTO)
- Paid Holidays
- Life Insurance
- Birthday PTO
- Education Reimbursement
Company Overview