Role Description
We are seeking a mid-level Data Engineer to join our AI team. In this role, you will build, operate, and enhance the data infrastructure supporting our Agentic AI initiatives. You will collaborate with ML engineers, AI scientists, and product managers to deliver reliable data pipelines that enable autonomous and semi-autonomous AI agents. As part of the R&DS AI Innovation Program, you will contribute to production-ready, secure, and compliant data solutions while progressively growing toward deeper architectural ownership.

Key Responsibilities

Mandatory

• Design, develop, and maintain scalable data pipelines and ETL/ELT processes supporting AI research, prototyping, and production use cases.
• Collaborate with AI scientists and engineers to translate data requirements into ingestion, transformation, and serving solutions.
• Apply data governance and security controls ensuring compliance, auditability, and protection of sensitive information.
• Monitor, troubleshoot, and resolve data pipeline failures, performance issues, and schema changes.
• Continuously improve reliability through testing, observability, documentation, and automation.
• Design and maintain efficient data models (e.g., star schemas, feature-ready datasets, semantic layers) supporting analytics, ML workflows, and AI agent operations.
• Implement automated data validation, schema checks, and pipeline testing to ensure high-quality data delivery across systems.

Preferred

• Contribute to data architectures supporting agent workflows, including training data preparation, retrieval layers, and inference logging.
• Build and enhance pipelines supporting near real-time agent interactions and feedback signals.
• Strong SQL skills, with experience designing analytical queries and working with relational and NoSQL databases.
• Implement and operate vector embedding stores, knowledge graph ingestion pipelines, and retrieval mechanisms.
• Implement data quality controls suitable for ML/LLM pipelines in regulated environments.
• Assist with performance tuning to reduce latency in agent-driven workflows.
• Familiarity with infrastructure-as-code and automated deployment for data pipelines.

Qualifications

Education

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.

Experience

• Typically 3+ years of professional experience in data engineering, including production-grade pipeline development.

Programming & Technologies

• Strong proficiency in Python; working experience with Java or Scala.
• Solid knowledge of SQL and experience with NoSQL databases.
• Familiarity with data warehousing and lakehouse platforms.

Cloud & Data Platforms

• Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP).
• Experience with orchestration frameworks and CI/CD practices for data pipelines.

Preferred Qualifications

• Familiarity with vector databases and embedding lifecycle management.
• Experience with containerization and orchestration tools (Docker, Kubernetes).
• Understanding of RAG data pipelines, LLM fine-tuning datasets, and evaluation signals.
• Exposure to streaming or event-driven data processing systems.