AI Engineer - Databricks Data Pipelines (On-site)

Apex Systems; Houston (TX); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Medical, Dental, Vision

ESPP

401K with company match

HSA

Employee Assistance Program

Job summary

Everforth Apex Systems in Houston is seeking an AI Engineer to design, build, and support AI-enabled data solutions on the Databricks platform, using SQL, Python, and PySpark to deliver production-ready pipelines.

You will collaborate with data engineers, analysts, and business stakeholders to translate requirements into scalable architectures, apply modern software practices, and troubleshoot production incidents.

Qualifications

• Deep expertise in writing and debugging complex SQL queries (CTEs, window functions, joins).
• Hands-on experience with Python and PySpark in data engineering contexts.
• Proficiency building declarative pipelines (Delta Live Tables or Lakeflow) and version control.

Responsibilities

• Design, develop, and productionize AI and ML solutions using Databricks and its AI capabilities.
• Build and maintain Databricks data workflows with Lakeflow and Delta Live Tables.
• Develop scalable data pipelines to support AI, analytics, and downstream reporting.
• Perform advanced SQL analysis across complex datasets and optimize queries.
• Investigate and optimize poorly performing SQL queries and execution patterns.
• Collaborate with cross-functional teams to translate requirements into technical solutions.
• Apply modern software engineering practices including testing, review, and deployment discipline.
• Support production incidents and troubleshoot AI solutions and SQL workloads.

Skills

Databricks

SQL

Python

PySpark

Delta Live Tables

Lakeflow

Delta Lake

Education

Bachelor's degree

Job description

Everforth Apex Systems in Houston is seeking an AI Engineer to design, build, and support AI-enabled data solutions on the Databricks platform, using SQL, Python, and PySpark to deliver production-ready pipelines.

You will collaborate with data engineers, analysts, and business stakeholders to translate requirements into scalable architectures, apply modern software practices, and troubleshoot production incidents.