AI Systems & Data Engineer

HyperFi•San Francisco, CA

96d

About The Position

We are seeking an AI Systems & Data Engineer to join our team. We are building a fast, flexible, and complex platform with a robust, event-driven architecture. This role requires expertise in building data pipelines within the Databricks environment, specifically for ingesting unstructured data, and leveraging that data to build AI agents. You’ll be a crucial member of rolling out products that will have immediate impact.

Requirements

5-7 years of experience building production-grade ML, data, or AI systems.
Strong grasp of prompt engineering, context construction, and retrieval design.
Comfortable working in LangChain and building agents.
Experience with PySpark and Databricks to handle real-world data scale.
Ability to write testable, maintainable Python with clear structure.
Understanding of model evaluation, observability, and feedback loops.
Excited to push from prototype → production → iteration.
Familiarity with Databricks Data Intelligence Platform which unifies data warehousing and AI use cases on a single platform.
Knowledge of Unity Catalog for open and unified governance of data, analytics, and AI on the lakehouse.
Understanding of data security concerns related to AI and how to mitigate them using the Databricks AI Security Framework (DASF).
Confident English skills to collaborate clearly and effectively with teammates

Nice To Haves

Have built scalable agent-like workflows on the Databricks platform.
Have worked on semantic chunking, vector search, or hybrid retrieval strategies.
Can walk us through a real-world prompt failure and how you fixed it.
Have contributed to OSS tools or internal AI platforms.
Think of yourself as both an engineer and a systems designer.
Are familiar with the concept of a data lakehouse architecture.

Responsibilities

Design and build data pipelines in Databricks for ingesting unstructured data.
Construct retrieval-augmented generation (RAG) systems from scratch using ingested data.
Build agentic LLM pipelines utilizing frameworks like LangChain, LangGraph, and LangSmith.
Own orchestration of PySpark and Databricks workflows to prepare inputs and track outputs for AI models.
Instrument evaluation metrics and telemetry to guide the evolution of prompt strategies.
Work alongside product, frontend, and backend engineers to tightly integrate AI into user-facing flows.
Leverage Databricks features such as Auto Loader for automatic detection of new files on cloud storage and schema changes.
Utilize Delta Lake for reliability, security, and performance on the data lake for streaming and batch operations.
Apply Databricks Workflows for orchestrating tasks to integrate data.
Implement Delta Live Tables for building reliable data pipelines with a declarative approach.