ML Engineer Gen AI & LLM

Mission

The Machine Learning (ML) Practice team at Databricks is a specialized, customer-facing unit experiencing significant demand for Large Language Model (LLM)-based solutions. We provide professional services to assist customers in building, scaling, and optimizing ML pipelines, and deploying them into production. Our team collaborates cross-functionally with engineering, product, and developer relations to shape strategic priorities and initiatives, and supports internal subject matter expert (SME) teams. We foster a collaborative environment where individuals with unique specializations contribute to the team's collective strength. This role is ideal for those who enjoy customer interaction, teamwork, and staying ahead of the curve in LLMs, MLOps, and broader ML trends. This role can be remote.

The Impact You Will Have

• Develop LLM solutions for customer data, including RAG architectures for enterprise knowledge repositories, natural language querying of structured data, and content generation.
• Build, scale, and optimize customer data science workloads, applying best-in-class MLOps to productionize these across diverse domains.
• Advise data teams on data science architecture, tooling, and best practices.
• Present findings and insights at conferences, such as Data+AI Summit.
• Provide technical mentorship to the broader ML SME community within Databricks.
• Collaborate with 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, utilizing tools like HuggingFace, Langchain, and OpenAI.
• A minimum of 5 years of hands-on industry data science experience, utilizing standard ML and data science tools such as pandas, scikit-learn, and TensorFlow/PyTorch.
• Proven experience building production-grade machine learning deployments on cloud platforms including AWS, Azure, or GCP.
• A graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience.
• Demonstrated ability to communicate and teach technical concepts to both technical and non-technical audiences.
• A passion for collaboration, continuous learning, and driving business value through ML.
• Preferred: Experience working with Databricks & Apache Spark for processing large-scale distributed datasets.