Staff AI Data Engineer

Cleveland Headquarters Office • Highland Heights, OH

4d

About The Position

Mid-level engineer who designs and maintains scalable data pipelines, ETL processes and data platforms to support AI/ML workloads, integrating vector stores and ensuring data quality and compliance.

Requirements

• At least two (2) years’ experience working in a Data Engineering, Data Science, Software Development or other relevant role.
• Professional experience with programming in either Python or an object-oriented programming language.
• Strong knowledge of relational and NoSQL-based databases, with significant proficiency in SQL.
• Understanding of ETL processes and data modeling concepts.
• Knowledge of data warehousing, lakehouse architectures, and data modeling concepts.
• General understanding of AI/ML concepts with the ability and willingness to learn more.
• Ability to collaborate with team leadership and Data Engineering, Infrastructure, AI Engineering, Security and Business peers.
• Strong problem-solving, communication and teamwork skills.

Nice To Haves

• Experience with big data technologies (Spark, Hadoop) and flow tools (Kafka, NiFi) is a plus but not required.
• Experience with one or more of these tools or services is a plus but not required.
• Experience with ML tools such as pytorch is a plus.
• Exposure to containerization (Docker), Kubernetes and continuous integration/continuous deployment (CI/CD). Experience is a plus but not required.

Responsibilities

• Implement and maintain scalable batch and streaming data pipelines to ingest, transform and serve data for AI/ML workloads; work with senior engineers and architects on designing pipelines and processes.
• Develop ETL/ELT processes using Python and SQL to prepare training, test, and production datasets and feature stores.
• Build and maintain data warehouses and lakes; integrate with vector stores to support retrieval-augmented generation (RAG) systems.
• Partner with more senior engineers to collaborate with AI Data Engineering, IT Data Engineering, Infrastructure, AI Engineering, Security and Business Leaders to deliver features for model training and inference.
• Implement data validation and quality checks with validation from more senior engineers; maintain documentation of data flows and schemas.
• Work with more senior engineers to ensure pipelines meet data quality, observability, security and regulatory compliance standards.
• Work with Model Context Protocol (MCP) to integrate into data pipelines and make modifications to existing MCP connections with guidance from more senior engineers.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed