AI Data & Infrastructure Engineer

What you'll do

• Design and maintain scalable AI data platforms, services, and APIs to support Agentic AI workflows.
• Build data ingestion, transformation, and publishing pipelines for structured, unstructured, and multimodal data.
• Implement Retrieval-Augmented Generation (RAG) pipelines, embedding workflows, and vector database integrations.
• Develop tools to make complex AI systems observable, understandable, and debuggable.
• Optimize platform scalability, reliability, performance, security, and cost across cloud-native environments.
• Build and integrate infrastructure for manufacturing workflow automation through Agentic AI capabilities.
• Translate complex user workflows into practical technical solutions and production-ready data systems.

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.
• Strong programming skills in Python and SQL, with proficiency in Java or Scala (preferred).
• Experience with Airflow, Kubeflow, or MLflow to build and orchestrate scalable AI data pipelines.
• Experience building batch and streaming data pipelines using Spark, Kafka, Airflow, Ray, Pandas, and modern lakehouse architectures.
• Knowledge of RAG architectures, embedding generation, vector databases, and AI data preparation for LLMs and agentic AI (preferred).
• Experience with cloud platforms (AWS, Azure, or GCP), Kubernetes, Docker, CI/CD, and Infrastructure as Code.
• Experience with API design, manufacturing/industrial data platforms, and leading technical initiatives (preferred).