Databricks
AI Engineer - FDE (Forward Deployed Engineer)
About this role
Databricks seeks an experienced AI Engineer to join their Forward Deployed Engineering team, delivering professional services engagements that help customers build and deploy production-grade GenAI and LLM applications at scale. This role requires demonstrated expertise in designing and productionizing cutting-edge AI solutions, with a focus on customer collaboration and thought leadership across multiple domains.
What you'll do
- Develop and deploy production-grade GenAI solutions using techniques like RAG, multi-agent systems, and fine-tuning
- Lead production rollouts of consumer and internally-facing GenAI applications with focus on evaluation and optimization
- Serve as technical advisor to customers across various industries and use cases
- Collaborate cross-functionally with product and engineering teams to influence roadmap priorities
- 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)
- Frameworks and tools (HuggingFace, LangChain, DSPy)
- Production ML deployment on AWS, Azure, or GCP
- Data science libraries (pandas, scikit-learn, PyTorch)
- Databricks Intelligence Platform and Apache Spark
- Technical communication and teaching to diverse audiences
- Large-scale distributed data processing
- ML 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
- Walk us through a GenAI application you deployed to production—what challenges did you face and how did you optimize performance?
- How do you approach evaluating and measuring the success of a production LLM application?