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 GenAI and LLM applications. This customer-facing role requires proven expertise in designing and scaling AI solutions, with opportunities to influence product strategy and establish thought leadership.
What you'll do
- Develop and deploy production GenAI solutions using cutting-edge techniques, including RAG, multi-agent systems, and fine-tuning
- Own end-to-end production rollouts of consumer and internal GenAI applications
- Serve as technical advisor to enterprise customers across diverse domains
- Collaborate cross-functionally with product and engineering teams to shape roadmap priorities
- Present at industry conferences and establish thought leadership on GenAI and LLMOps
- Travel to customer sites approximately once every 4-8 weeks
What they're looking for
- GenAI application development (RAG, multi-agent systems, Text2SQL, fine-tuning)
- Production GenAI deployment, evaluation, and optimization
- Python data science tools (pandas, scikit-learn, PyTorch)
- GenAI frameworks (HuggingFace, LangChain, DSPy)
- AWS, Azure, or GCP cloud platforms
- Databricks and Apache Spark (preferred)
- Technical communication to diverse audiences
- Machine learning production deployments
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 were the key challenges and how did you optimize for performance?
- How have you approached evaluating and measuring the quality of GenAI systems in production?