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 to help customers build and deploy production-grade generative AI applications. This customer-facing role requires deep expertise in GenAI, LLMs, and machine learning at scale, with responsibilities spanning solution development, technical advisory, and cross-functional collaboration.
What you'll do
- Develop and deploy cutting-edge GenAI solutions incorporating latest research techniques to solve customer problems
- Own end-to-end production rollouts of consumer and internally facing GenAI applications
- Serve as technical advisor to customers across multiple domains on AI strategy and implementation
- Present at industry conferences and establish thought leadership internally and externally
- Collaborate with product and engineering teams to influence roadmap priorities
- 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)
- LLM frameworks and tools (HuggingFace, LangChain, DSPy)
- Production ML deployment on AWS, Azure, or GCP
- Data science tools (pandas, scikit-learn, PyTorch)
- Databricks Intelligence Platform and Apache Spark
- Technical communication and teaching abilities
- Python and software engineering practices
- ML model evaluation and optimization
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 built from conception to production—what were the key challenges in evaluation and optimization?
- Walk us through your experience deploying ML systems at scale on a cloud platform; how did you handle monitoring and optimization?