Senior Data Scientist / Machine Learning Engineer - GenAI

Req ID: CSQ326R77

Location: London

Mission

The Machine Learning (ML) Practice team is a highly specialized customer-facing ML team at Databricks facing an increasing demand for Large Language Model (LLM)-based solutions. We deliver professional services engagements to help our customers build, scale, and optimize ML pipelines, as well as put those pipelines into production. 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 LLMs, MLOps, and ML more broadly.

The impact you will have:

Develop LLM solutions on customer data, such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, and content generation

Build, scale, and optimize customer data science workloads and apply best-in-class MLOps to productionize these workloads across a variety of domains
Advise data teams on various data science topics such as architecture, tooling, and best practices
Present at conferences such as Data+AI Summit
Provide technical mentorship to the larger ML SME community in Databricks
Collaborate cross-functionally with the product and engineering teams to define priorities and influence the product roadmap 

What we look for:

Experience building Generative AI applications, including RAG, agents, text2sql, fine-tuning, and deploying LLMs, with tools such as HuggingFace, Langchain, and OpenAI

Extensive hands-on industry data science experience, leveraging typical machine learning and data science tools including pandas, scikit-learn, and TensorFlow/PyTorch
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 communicating and/or teaching technical concepts to non-technical and technical audiences alike
Passion for collaboration, life-long learning, and driving business value through ML
[Preferred] Experience working with Databricks & Apache Spark to process large-scale distributed datasets
As a client-facing role, travel may be necessary to support meetings and engagements.