AI Data Engineer

The AI Data Engineer designs, builds, and maintains data pipelines and integrations that transform structured and unstructured data into enterprise-ready assets to power AI and analytics solutions. This role plays a critical part in bridging raw data sources with generative AI platforms and applications, ensuring data quality, compliance, and scalability. The AI Data Engineer collaborates with university stakeholders, IT teams, and academic units to deliver reliable, secure, and innovative data solutions that advance Syracuse University's mission.

Education and Experience:

• Bachelors degree in Artificial Intelligence, Computer Science, Data Science, or related field, or equivalent combination of education and experience.
• 4+ years of experience in data engineering, AI/ML integration, or enterprise IT.
• Experience in higher education IT environments preferred.

Skills and Knowledge:

• Expertise in data wrangling for structured and unstructured data.
• Proficiency in SQL and at least one programming language (Python, Java, or C#).
• Familiarity with API development, microservices, and Model Context Protocol (MCP) integrations.
• Experience with cloud infrastructure (Azure, AWS, GCP), containerization (Docker, Kubernetes), and serverless platforms (e.g., Logic Apps).
• Familiarity in deploying AI/ML platforms (Azure AI Foundry, Google Vertex, Amazon Bedrock, OpenAI, etc.).
• Understanding of data governance, privacy, and ethical AI principles.
• Strong problem-solving, communication, and collaboration skills.

Responsibilities:

Data Engineering & Pipeline Development:

• Design and implement scalable pipelines that ingest, clean, transform, and aggregate data from diverse sources (ERP, LMS, research systems, APIs, and external datasets) into formats optimized for AI and analytics.
• Ensure data quality, integrity, and reproducibility through robust engineering practices.

Integration & Platform Support:

• Build connectors, workflows, and APIs to unify and operationalize data across cloud and on-premises platforms.
• Support deployment and lifecycle management of AI/ML models, ensuring seamless integration with enterprise applications.

Governance, Security & Compliance:

• Maintain metadata, lineage, and documentation to support transparency and auditability.
• Ensure adherence to Syracuse University's ISF, FERPA, HIPAA, and other regulatory requirements, while applying ethical AI and data governance principles.

Collaboration & Stakeholder Engagement:

• Partner with academic and administrative units to understand AI and data needs, delivering tailored solutions aligned with institutional priorities.
• Act as a liaison between AI/ML teams and business units to maximize value from data assets.

Innovation & Mentorship:

• Provide technical mentorship on data engineering and integration best practices.
• Stay ahead of emerging technologies (e.g., MCP, serverless, containerization, and generative AI) to drive innovation in data and AI adoption.