Syracuse University needs to design, build, and maintain data pipelines and integrations that transform structured and unstructured data into enterprise-ready assets to power AI and analytics solutions, bridging raw data sources with generative AI platforms and applications, ensuring data quality, compliance, and scalability.
Requirements
- Expertise in data wrangling for structured and unstructured data.
- Proficiency in SQL and at least one programming language (Python, Java, or C-Sharp).
- 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.
Responsibilities
- 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.
- 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.
- 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.
- Stay ahead of emerging technologies (e.g., MCP, serverless, containerization, and generative AI) to drive innovation in data and AI adoption.
Other
- Bachelor’s 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.
- Strong problem-solving, communication, and collaboration skills.
- Partner with academic and administrative units to understand AI and data needs, delivering tailored solutions aligned with institutional priorities.
