Founding Ai Infrastructure Engineer
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