Overview
- Build and deliver AI solutions: take ownership of developing high‑performance AI features to help users discover, organize, and optimize access to large datasets.
- Rapid experimentation and iteration: implement a highly iterative process where you quickly prototype, test, and validate with real users, shipping and evolving LLM‑ or agent‑powered workflows for the data engineering lifecycle.
- Collaborate: work with the rest of the team to shape AI‑driven product features, including integrating agentic components with internal tools like Slack and alerting systems while engaging with internal data teams for dogfooding.
- Utilize AI tools effectively: use AI and automation tools to enhance both product functionality and your own development workflows.
- Effective communication: thrive in a dynamic and collaborative environment, communicating effectively across teams.
- Ownership and impact: take full ownership of the AI solutions you develop, ensuring they are innovative, scalable, maintainable, and aligned with real user workflows.
Requirements
- Experience with LLMs, context engineering, and building applications powered by GenAI.
- Proven track record of delivering software that made it into production and is actively used by users.
- Exposure to working in cloud‑native environments (e.g., AWS, GCP, Azure).
- Experience using observability tools to understand and troubleshoot system behavior.
Core Competencies
Demonstrates expertise in developing AI solutions, particularly with LLMs and GenAI, while effectively collaborating across teams to enhance product functionality and user experience. Proven ability to take ownership of scalable and maintainable software solutions in cloud‑native environments.
