AI Data Engineer

The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.

Company Overview

A leading global technology company is seeking an AI Data Engineer to help build and scale production-grade AI solutions within a global legal operations environment. The organization is focused on developing advanced AI applications powered by reliable, high-quality data infrastructure. This role plays a critical part in ensuring AI systems receive accurate, timely, and well-structured data to support intelligent decision-making and enterprise-scale operations.

The AI Data Engineer will own the pipelines, data feeds, and integration infrastructure that power AI applications. Working closely with AI and data teams, this position is responsible for building and maintaining robust data architectures that connect enterprise systems to AI platforms while ensuring reliability, scalability, and performance.

Key Responsibilities

• Design and implement data pipelines that ingest, transform, and deliver data from legal systems such as matter management, eBilling, contract lifecycle management (CLM), and document management platforms to AI applications.
• Build and maintain pipelines that load and refresh vector databases, document stores, and graph databases used by AI retrieval systems.
• Engineer data transformations that prepare legal data for AI consumption, including chunking, embedding generation, metadata enrichment, and schema normalization.
• Develop integrations with Model Context Protocol (MCP), vector databases, and knowledge graphs to support context engineering and AI retrieval systems.
• Build and maintain APIs that expose structured and unstructured data to AI applications and analytics tools.
• Implement data quality checks and validation processes to ensure AI systems receive reliable and complete data.
• Develop monitoring and alerting mechanisms for pipeline health, data freshness, and processing failures.
• Optimize data delivery and architecture based on AI data access patterns and performance requirements.
• Integrate with semantic layers by leveraging entity resolution outputs, taxonomy mappings, and enriched datasets to improve AI grounding.
• Implement ETL/ELT processes using tools such as dbt, Fivetran, Airflow, or similar technologies.
• Create and maintain documentation for pipeline architectures, data contracts, and operational runbooks.

Required Qualifications

• Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field, or equivalent practical experience.
• Master’s degree preferred.
• 4+ years of experience in data engineering supporting AI applications.
• Strong proficiency in SQL and Python for data engineering and data transformation.
• Experience with cloud data platforms such as Snowflake, Databricks, BigQuery, or similar technologies.
• Experience with ETL/ELT tools including dbt, Fivetran, Airflow, or comparable solutions.
• Experience building and maintaining REST APIs.
• Strong understanding of data modeling principles and transformation best practices.
• Experience using version control systems such as Git and implementing CI/CD practices.
• Ability to collaborate closely with AI and machine learning teams to support evolving data requirements.

Preferred Qualifications

• Experience with vector databases such as Pinecone, Weaviate, Chroma, or similar platforms.
• Experience building embedding generation pipelines and integrating document stores such as MongoDB or equivalent technologies.
• Understanding of Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and context engineering principles.
• Experience with semantic layer technologies such as dbt Semantic Layer, Cube, AtScale, or similar solutions.
• Familiarity with knowledge graphs, Neo4j, and ontology design concepts.
• Experience with streaming or event-driven architectures using Kafka or similar technologies.
• Familiarity with legal operations systems, including matter management, eBilling, contract lifecycle management, and document management platforms.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
2. No Client Relationship: This company is not a client of SpringCube unless stated.
3. To Apply: Click the Apply button to be redirected to the hiring company’s application page for this job.
4. No Liability: SpringCube is not liable for inaccuracies.