AI Data Engineer

Imagine what you could do here. At Apple new ideas have a way of becoming great products services and customer experiences very quickly. Bring passion and dedication to your job and theres no telling what you could you passionate about building the data pipelines that make AI systems fast accurate and reliablenDo you thrive on engineering clean data flows that connect enterprise systems to intelligent applicationsnCan you build infrastructure thats both production-grade and purpose-built for AI consumptionnnThe Applied Data Science team within Legal Operations is building production-grade AI for a global legal organization and every AI system is only as good as the data flowing into it. The AI Data Engineer owns the pipelines data feeds and integration infrastructure that ensure AI applications have the right data in the right form at the right time.n

The AI Data Engineer builds and maintains the data infrastructure that powers AI applications across Legal Operations. You will design and implement data pipelines that ingest from legal systems transform data into AI-ready formats load vector databases and other AI stores and expose data services through APIs. This role is embedded within the AI team and works in close partnership with AI and data colleagues to ensure AI systems have reliable high-quality data at every and implement data pipelines that ingest transform and deliver data from legal systems (matter management eBilling CLM document management) to AI applicationsntBuild and maintain pipelines that load and refresh vector databases document stores and graph databases used by AI retrieval systemsntEngineer data transformations that prepare legal data for AI consumption chunking embedding generation metadata enrichment and schema normalizationntBuild upstream and downstream integrations with MCP (Model Context Protocol) vector databases and knowledge graphs to support context engineering and AI retrieval systemsntDevelop and maintain APIs that expose structured and unstructured data to AI applications and analytics toolsntImplement data quality checks and validation at pipeline ingestion points to ensure AI systems receive reliable complete datantBuild monitoring and alerting for pipeline health data freshness and load failuresntUnderstand AI data access patterns and optimize data delivery for AI performancentIntegrate with the semantic layer consuming entity resolution outputs taxonomy mappings and enriched datasets to ground AI applicationsntImplement ETL/ELT processes using dbt Fivetran or similar tools with a focus on reliability and maintainabilityntDocument pipeline designs data contracts and operational runbooksn

Bachelors degree in Computer Science Data Science Information Systems or related field (or equivalent experience); Masters degree preferredn4 years of experience in data engineering related to AI applicationnStrong proficiency in SQL and Python for data engineering and transformationnExperience with cloud data platforms (Snowflake Databricks BigQuery or similar)nExperience with ETL/ELT tools (dbt Fivetran Airflow or similar)nExperience building and maintaining REST APIsnUnderstanding of data modeling and data transformation best practicesnExperience with version control (Git) and CI/CD practicesnAbility to work closely with AI/ML teams and understand their data requirementsn

Experience with vector databases (Pinecone Weaviate Chroma) embedding generation pipelines document stores (MongoDB or similar) and their integration patternsnnUnderstanding of RAG MCP architectures context engineering principles and how data quality affects retrieval performancennExperience with semantic layer technologies (dbt Semantic Layer Cube AtScale) knowledge graphs (Neo4j) or ontology designnnExperience with streaming or event-driven data architectures (Kafka or similar)nnFamiliarity with legal operations data (matter management eBilling CLM document management)n