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 productionize GenAI and LLM applications at scale. This customer-facing role requires demonstrated expertise in production GenAI deployment, ML engineering, and cross-functional collaboration to influence product strategy.
What you'll do
- Develop and deploy production-grade GenAI solutions using techniques like RAG, multi-agent systems, and fine-tuning
- Own end-to-end rollouts of consumer and internally-facing GenAI applications
- Serve as a technical advisor to customers across various domains and industries
- Collaborate with product and engineering teams to shape roadmap priorities
- Present at conferences and establish thought leadership in AI/ML
- Travel to customer sites approximately 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 and ML frameworks (PyTorch, scikit-learn, pandas, HuggingFace, LangChain, DSPy)
- Databricks Intelligence Platform and Apache Spark (preferred)
- Model evaluation and optimization techniques
- Technical communication to diverse audiences
- Distributed data processing at scale
- LLMOps and ML systems design
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 technical challenges and how did you address them?
- How do you approach evaluating and optimizing GenAI systems for production quality?