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Databricks

AI Engineer - FDE (Forward Deployed Engineer)

Boston, MassachusettsFrom $210.2kmidAdded today

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 applications. This customer-facing role combines technical expertise in LLMs and GenAI with strategic advisory responsibilities, requiring demonstrated experience shipping AI systems at scale.

What you'll do

  • Develop and deploy production GenAI solutions using techniques from Databricks AI research to address customer challenges
  • Own end-to-end rollouts of consumer and internal GenAI applications, including evaluation and optimization
  • Serve as technical advisor to customers across multiple domains, providing strategic guidance on AI implementation
  • Collaborate cross-functionally with product and engineering teams to influence roadmap priorities
  • Present at industry conferences and establish thought leadership on GenAI and LLMOps
  • Travel to customer sites every 4-8 weeks as needed for engagements

What they're looking for

  • GenAI application development (RAG, multi-agent systems, Text2SQL, fine-tuning)
  • LLMOps frameworks and tools (HuggingFace, LangChain, DSPy)
  • Production ML deployment on AWS, Azure, or GCP
  • Data science tooling (pandas, scikit-learn, PyTorch)
  • Databricks Intelligence Platform and Apache Spark (preferred)
  • Technical communication and teaching to diverse audiences
  • Large-scale distributed data processing
  • ML evaluation and optimization techniques
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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
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Likely interview questions

  • Describe a GenAI application you deployed to production—what were the key technical challenges and how did you address evaluation and optimization?
  • Tell us about your experience with RAG or multi-agent systems. How did you handle production considerations like latency and reliability?
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