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Founding Ai Infrastructure Engineer

🌐 Remote💼 Full-time🗓 2026-07-30

Core

Design and build the scalable data foundation powering AI products, including pipelines, databases, and infrastructure for training and inference.

Role type

Founding Data Infrastructure Engineer

Builds

Data pipelines, databases, data warehouses, vector databases, and cloud infrastructure for AI applications

Domain

AI/ML infrastructure, Data Engineering, Cloud Computing

Deliverable

infrastructure

Required skills

Python, SQL, Data processing frameworks (Pandas, PySpark, Airflow), Database design, Cloud infrastructure (AWS, GCP, Azure), Git/GitHub, Data pipeline development, API development

Preferred skills

Vector databases (Pinecone, Weaviate, Qdrant), LLM systems and RAG architectures, Kubernetes, Docker, Analytics platform development

Technologies

Python, Pandas, PySpark, Airflow, PostgreSQL, MongoDB, BigQuery, Snowflake, AWS, GCP, Azure, Git, GitHub, Pinecone, Weaviate, Qdrant, Kubernetes, Docker

Responsibilities

Design and build scalable data pipelines for collecting, processing, and storing structured and unstructured data; Develop ETL/ELT workflows to support analytics, machine learning, and AI applications; Build and maintain databases, data warehouses, and vector databases; Design data architectures that ensure reliability, scalability, and security; Work closely with AI engineers to prepare, clean, and manage training and inference data; Implement monitoring, logging, and data quality systems; Optimize data infrastructure for performance and cost efficiency; Support cloud deployments and infrastructure on AWS, GCP, or Azure; Establish best practices for data governance, documentation, and engineering processes

Seniority

Founding, hands-on IC with significant ownership

Rewrite
## About Metry AI Metry AI is a technology startup based in Japan, Taiwan and Canada, building intelligent systems that help businesses work smarter. Our current focus is on developing AI-driven tools that connect empathy, design, and automation, serving markets in Taiwan and Japan. We are a small, ambitious, and fast-moving team building the future of AI-powered customer experiences. As a Founding Data Infrastructure Engineer, you will work directly with the founders to design and build the data foundation that powers our AI products. ## Responsibilities - Design and build scalable data pipelines for collecting, processing, and storing structured and unstructured data. - Develop ETL/ELT workflows to support analytics, machine learning, and AI applications. - Build and maintain databases, data warehouses, and vector databases. - Design data architectures that ensure reliability, scalability, and security. - Work closely with AI engineers to prepare, clean, and manage training and inference data. - Implement monitoring, logging, and data quality systems. - Optimize data infrastructure for performance and cost efficiency. - Support cloud deployments and infrastructure on AWS, GCP, or Azure. - Establish best practices for data governance, documentation, and engineering processes. ## Qualifications Required - Experience with Python and data processing frameworks (Pandas, PySpark, Airflow, etc.). - Strong understanding of SQL and database design. - Familiarity with PostgreSQL, MongoDB, BigQuery, Snowflake, or similar platforms. - Experience building data pipelines and APIs. - Understanding of cloud infrastructure (AWS, GCP, Azure). - Comfortable using Git/GitHub and collaborating in a startup environment. - Strong problem-solving skills and ability to work independently. ## Nice to Have - Experience with Vector Databases (Pinecone, Weaviate, Qdrant). - Experience with LLM systems and RAG architectures. - Experience with Kubernetes and Docker. - Experience building analytics or AI platforms. - Startup experience or side projects. ## What You'll Get - Opportunity to build the core data infrastructure of an AI startup from the ground up. - Direct collaboration with the founding team on product and technical strategy. - Significant ownership and technical decision-making responsibility. - Flexible work hours and remote-friendly environment. - Equity compensation and potential leadership opportunities as the company grows
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