Databricks

Data and AI

AIEngineer-FDE(ForwardDeployedEngineer)

$110000–165000k ~AI est. South Korea Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“AI Engineer - FDE (Forward Deployed Engineer) at Databricks”

Industry & Context.

Data and AI
Eligibility Requirements

Willing to travel

How You'll Work.

Communication Scope

Technical concepts

Full Job Description

CSQ427R171 Mission The AI Forward Deployed Engineering (AI FDE) team is a highly specialized customer-facing AI team at Databricks. We deliver professional services engagements to help our customers build and productionize first-of-its-kind AI applications. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams. We view our team as an ensemble: we look for individuals with strong, unique specializations to improve the overall strength of the team. This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for the latest trends in GenAI, LLMOps, and ML more broadly. The impact you will have: Develop cutting-edge GenAI solutions, incorporating the latest techniques from Databricks AI research to solve customer problems Own production rollouts of consumer and internally facing GenAI applications Serve as a trusted technical advisor to customers across a variety of domains Present at conferences such as Data + AI Summit, recognized as a thought leader internally and externally Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap What we look for: Experience building GenAI applications, including RAG, multi-agent systems, Text2SQL, fine-tuning, etc., with tools such as HuggingFace, LangChain, and DSPy Expertise in deploying production-grade GenAI applications, including evaluation and optimizations Extensive years of hands-on industry data science experience, leveraging common machine learning and data science tools, i. e. pandas, scikit-learn, PyTorch, etc. Experience building production-grade machine learning deployments on AWS, Azure, or GCP Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience Experience comm

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