The Machine Learning Practice team at Databricks is facing an increasing demand for Large Language Model-based solutions and needs a professional to help deliver professional services engagements to customers.
Requirements
- Experience building Generative AI applications, including RAG, agents, text2sql, fine-tuning, and deploying LLMs, with tools such as HuggingFace, Langchain, and OpenAI
- 5+ years of 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
- Experience working with Databricks & Apache Spark to process large-scale distributed datasets (Preferred)
- Experience with tools such as HuggingFace, Langchain, and OpenAI
- Experience with pandas, scikit-learn, and TensorFlow/PyTorch
Responsibilities
- 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 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
Other
- 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
