AI Engineer - FDE (Forward Deployed Engineer)
Databricks is a data and AI platform that lets organizations build analytics, AI agents, and applications on a unified, governed lakehouse.
Funding history
Investors
About Databricks
Data engineers, analysts, and AI teams use Databricks to process large datasets, build reliable pipelines, and train models on a single governed platform. Users can run SQL analytics, serve ML predictions in real time, and deploy AI agents grounded in enterprise data. Its open lakehouse architecture provides consistent security and governance across analytical and operational workloads.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will develop cutting-edge GenAI solutions using the latest Databricks AI research, own production rollouts of consumer and internal GenAI applications, advise customers, present at conferences and collaborate with product and engineering teams to shape the roadmap.
Requirements
- 7+ years of experience building GenAI applications, including RAG, multi-agent systems, Text2SQL, fine-tuning, with HuggingFace, LangChain, DSPy
- Expertise in deploying production-grade GenAI applications, including evaluation and optimizations
- Extensive hands-on industry data science experience with pandas, scikit-learn, PyTorch
- Experience building production-grade ML deployments on AWS, Azure, or GCP
- Graduate degree in a quantitative discipline or equivalent practical experience
- Experience communicating and teaching technical concepts
- Willing to travel once every 4-8 weeks to see customers
- Native-level fluency in Korean and professional working proficiency in English
- Preferred: Databricks Intelligence Platform and Apache Spark experience
Responsibilities
- Develop cutting-edge GenAI solutions to solve customer problems
- Own production rollouts of consumer and internally facing GenAI applications
- Serve as a trusted technical advisor to customers
- Present at conferences such as Data + AI Summit
- Collaborate with product and engineering teams to influence priorities and the roadmap