AI Engineer, AI FDE (Machine Learning Practice)

About the role

Databricks' Machine Learning (ML) Practice team is a customer-facing team supporting growing demand for Large Language Model (LLM) solutions. It delivers professional services engagements to help customers build, scale and optimize ML pipelines and put them into production, working with engineering, product and developer relations teams. The role can be remote.

Key responsibilities

• Develop LLM solutions on customer data, such as RAG architectures on enterprise knowledge repositories, natural-language querying of structured data and content generation
• Build, scale and optimize customer data science workloads and apply best-in-class MLOps to productionize them across domains
• Advise data teams on data science architecture, tooling and best practices
• Present at conferences such as Data+AI Summit
• Mentor the wider ML subject-matter-expert community in Databricks
• Work with product and engineering teams to define priorities and influence the product roadmap

Required profile

• 5+ years of hands-on industry data science experience
• Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience
• Experience building production-grade ML deployments on AWS, Azure or GCP
• Experience communicating and teaching technical concepts to both technical and non-technical audiences
• Preferred: experience with Databricks and Apache Spark for large-scale distributed datasets

Required skills

• Generative AI applications: RAG, agents, text2sql, fine-tuning and LLM deployment with HuggingFace, LangChain and OpenAI
• Data science tools such as pandas, scikit-learn and TensorFlow/PyTorch