Databricks
AI Engineer - FDE (Forward Deployed Engineer)
About this role
Databricks seeks an AI Engineer to join their Forward Deployed Engineering team, delivering professional services to help enterprise customers build and productionize generative AI applications. You'll work across technical domains including RAG, multi-agent systems, and LLM optimization while serving as a trusted advisor and thought leader.
What you'll do
- Develop and deploy production-grade GenAI solutions using frameworks like LangChain, HuggingFace, and DSPy
- Own end-to-end production rollouts of generative AI applications for customers and internal use
- Serve as technical advisor to customers across diverse industries and use cases
- Collaborate cross-functionally with product and engineering teams to influence roadmap priorities
- Present at industry conferences and establish thought leadership on GenAI topics
- Travel to customer sites approximately once every 4-8 weeks as needed
What they're looking for
- GenAI application development (RAG, multi-agent systems, Text2SQL, fine-tuning)
- Production ML deployment on AWS, Azure, or GCP
- Python data science stack (pandas, scikit-learn, PyTorch)
- LLMOps and model evaluation/optimization
- Databricks and Apache Spark (preferred)
- Technical communication to technical and non-technical audiences
- Machine learning systems design and MLOps
- Large-scale distributed data processing
Benefits
- Competitive salary range $152,900–$210,155 USD
- Annual performance bonus eligibility
- Equity compensation
- Remote work options across US
- Opportunities to present at major conferences
- Cross-functional collaboration with leading AI research team
Opens the official application on the employer’s site. No login required.
Databricks
Databricks builds a unified data and AI platform that combines database systems, distributed computing, and generative AI capabilities across multi-cloud infrastructure. The company is hiring software engineers, applied AI engineers, and web engineers to develop core database engines, ML/AI features, inference systems, and user-facing products.
- Website
- databricks.com
Likely interview questions
- Walk us through a GenAI application you've deployed to production—what were the key challenges and how did you handle evaluation and optimization?
- Describe your experience with RAG or multi-agent systems. How did you approach retrieval quality or agent reasoning?