Databricks
AI Engineer - FDE (Forward Deployed Engineer)
About this role
Databricks seeks an experienced AI Engineer to join its Forward Deployed Engineering team, delivering professional services to help customers build and deploy production-scale GenAI and LLM applications. You'll work cross-functionally with customers, product teams, and internal experts to implement cutting-edge AI solutions and serve as a technical thought leader.
What you'll do
- Develop production GenAI solutions using latest techniques including RAG, multi-agent systems, and fine-tuning
- Own end-to-end deployment and optimization of consumer and internally-facing GenAI applications
- Serve as trusted technical advisor to customers across various domains and industries
- Collaborate with product and engineering teams to influence roadmap priorities and shape strategic initiatives
- Present at industry conferences and establish thought leadership internally and externally
- 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)
- ML frameworks and libraries (HuggingFace, LangChain, DSPy, PyTorch, scikit-learn, pandas)
- Production ML deployment on AWS, Azure, or GCP
- Databricks Intelligence Platform and Apache Spark (preferred)
- Data science and hands-on ML engineering with industry experience
- Technical communication and teaching to diverse audiences
- Distributed data processing and large-scale system design
- GenAI evaluation and optimization techniques
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
- Describe a GenAI application you deployed to production—what challenges did you face with evaluation and optimization?
- How have you used RAG or multi-agent systems to solve a real customer problem, and how did you measure success?