AI Data Platform Engineer

What you'll do

• Design and maintain scalable AI data platforms, services, and APIs to support model development and production.
• Develop ingestion, transformation, and publishing pipelines for structured, unstructured, and multimodal data.
• Build AI-ready datasets through ground truth creation, curation, annotation workflows, and versioning.
• Create data quality frameworks, validation pipelines, and observability metrics to ensure trusted datasets.
• Implement RAG pipelines, embedding workflows, and vector database integrations for enterprise applications.
• Manage the end-to-end AI data lifecycle including metadata management, lineage, and secure publishing.
• Optimize platform scalability, performance, security, and cost across cloud-native environments.
• Evaluate emerging technologies to improve capabilities for GenAI, Agentic AI, and Embodied AI solutions.

What we're looking for

• Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or a related field.
• Experience designing and building scalable data platforms and distributed systems.
• Proficiency in Python and SQL, with preferred proficiency in Java or Scala.
• Experience using Airflow, Kubeflow, or MLflow to build and orchestrate scalable AI data pipelines.
• Experience building batch and streaming pipelines using Spark (PySpark), Kafka, Ray, and modern lakehouse architectures like Iceberg or Delta Lake.
• Hands-on experience with AI data engineering including ground truth creation, curation, annotation workflows, versioning, and metadata management.
• Knowledge of RAG architectures, embedding generation, vector databases, and AI data preparation for LLMs and agentic AI.
• Experience with cloud platforms (AWS, Azure, or GCP), Kubernetes, Docker, CI/CD, and Infrastructure as Code.