Senior AI/ML Engineering Manager - Databricks

EY; Buffalo (NY); Hybrid; USD 125,000 - 230,000; Full time

Benefits offered by this job

Medical and dental coverage

Pension and 401(k)

Paid time off

Job summary

EY seeks a Manager in Model Engineering to lead AI engineering initiatives on the Databricks platform. You will drive solution design, data pipelines, and scalable AI systems while coordinating cross-functional teams across data science, engineering, and product stakeholders.

You will oversee production deployment, governance of ontologies and knowledge graphs, and ensure responsible deployment with measurable outcomes for client engagements in a hybrid work model.

Qualifications

• Bachelor’s degree required (4-year degree).
• 6–10 years of relevant experience in AI, Data Science, and/or Machine Learning.
• 2–4 years directly managing technical teams.
• 3–4 years building production AI/ML solutions on Databricks (Agent Bricks, Model Serving).
• Databricks Certification with multiple certifications (Data Engineer/ML/Generative AI).
• Strong skills in Python, PySpark, and/or Scala.

Responsibilities

• Lead AI workstreams from design through production deployment on Databricks.
• Manage multidisciplinary teams delivering AI engineering initiatives.
• Deliver production-grade data and AI pipelines, ontologies, and semantic models on Databricks.
• Collaborate with data scientists, engineers, and DevOps to sustain solutions.
• Engage with clients, lead workstreams, and drive measurable business value.

Skills

Databricks platform

Python

PySpark

Scala

DevOps tools

Agent Bricks

Product-centric development

Education

Bachelor's degree

Databricks Certification

Master's degree

Tools

Databricks

Git

Azure DevOps

Unity Catalog

Model Serving

MLflow

Databricks Apps / Lakebase Postgres

Job description

EY seeks a Manager in Model Engineering to lead AI engineering initiatives on the Databricks platform. You will drive solution design, data pipelines, and scalable AI systems while coordinating cross-functional teams across data science, engineering, and product stakeholders.

You will oversee production deployment, governance of ontologies and knowledge graphs, and ensure responsible deployment with measurable outcomes for client engagements in a hybrid work model.