Manager, AI Engineering & GenAI on Databricks

EY; Austin (TX); Hybrid; USD 126,000 - 230,000; Full time

Benefits offered by this job

Medical and dental coverage

Pension and 401(k)

Paid time off

Job summary

EY is seeking a Manager in Model Engineering to lead AI/ML initiatives on the Databricks platform, working with clients and cross-functional teams. You will supervise engineers and data scientists to design, build, and scale AI solutions across diverse data sources and ecosystems.

You will guide model governance, MLOps practices, and domain ontologies, ensuring responsible deployment and measurable business value in a hybrid, on-site/remote environment.

Qualifications

• Bachelor’s degree required (4-year degree).
• 6–10 years of AI, data science, and/or machine learning work experience.
• 2–4 years of direct technical team management experience.
• 3–4 years building/ deploying production AI/ML on Databricks.
• Databricks Certification (minimum 1 professional and 1 associate).
• Strong Python, PySpark, and/or Scala skills.

Responsibilities

• Lead AI workstreams from design to production deployment on Databricks.
• Manage multidisciplinary teams delivering data/AI pipelines and AI solutions.
• Collaborate with clients and Databricks teams to shape and deliver use cases.
• Design and govern ontologies, semantic models, and data products.
• Apply product-centric development to define user needs and measurable outcomes.
• Drive adoption, backlogs, and roadmaps with stakeholders.

Skills

Python

PySpark

Scala

Databricks

MLOps

AgentOps

Git

Azure DevOps

Model Serving

Unity Catalog

Education

Bachelor’s degree

Master’s degree (preferred)

Tools

Databricks

MLflow

Kubernetes

Vision/ML tooling

Job description

EY is seeking a Manager in Model Engineering to lead AI/ML initiatives on the Databricks platform, working with clients and cross-functional teams. You will supervise engineers and data scientists to design, build, and scale AI solutions across diverse data sources and ecosystems.

You will guide model governance, MLOps practices, and domain ontologies, ensuring responsible deployment and measurable business value in a hybrid, on-site/remote environment.