Senior AI Engineer: Generative AI on Databricks

EY; San Diego (CA); Hybrid; USD 107,000 - 177,000; Full time

Benefits offered by this job

Medical and dental coverage

Pension and 401(k) plans

Paid time off

Job summary

EY is seeking a Senior AI Engineer to lead scalable AI/ML deployments on the Databricks platform, collaborating with client teams and EY leadership to deliver generative AI solutions and enterprise data products.

You will design data pipelines, implement agentic AI, and monitor models to ensure reliability, governance, and business value. A hybrid onsite/remote work model with travel for client engagements is expected.

Qualifications

• Bachelor’s degree in a relevant field is required.
• 3–6 years of full-time working experience in AI and/or Machine Learning.
• Databricks Certification with at least 1 professional and 1 associate certification (Data Engineer/ML/Generative AI).
• Strong skills in Python, PySpark, and/or Scala.
• Experience designing, building, and maintaining production AI/ML solutions on Databricks.
• Experience with MLOps, LLMOps, and AgentOps and MLflow.
• Excellent communication and willingness to travel for client engagements.

Responsibilities

• Research and implement scalable machine learning, generative AI, and agentic systems on Databricks.
• Build data engineering pipelines and AI solutions on Databricks.
• Monitor and optimize learning processes using MLflow and tracing.
• Design and govern ontologies and semantic models for AI solutions and enterprise data products.
• Collaborate with client teams and travel as needed.

Skills

Python

PySpark

Scala

Databricks

SQL

Machine Learning

Generative AI

MLOps

Education

Bachelor’s degree

Tools

MLflow

Unity Catalog

Model Serving

Databricks CLI/SDKs

Git

Azure DevOps

Job description

EY is seeking a Senior AI Engineer to lead scalable AI/ML deployments on the Databricks platform, collaborating with client teams and EY leadership to deliver generative AI solutions and enterprise data products.

You will design data pipelines, implement agentic AI, and monitor models to ensure reliability, governance, and business value. A hybrid onsite/remote work model with travel for client engagements is expected.