Job Title: AI/ML Architect with Databricks

Location : Los Angeles CA (Hybrid)

Role Overview

We are seeking a skilled AI/ML Architect with hands-on experience in Databricks to join our team. The ideal candidate has strong analytical capabilities, experience building scalable data pipelines and machine learning models, and the ability to collaborate with cross-functional teams to drive data-driven decision-making.

This role involves working with large datasets, advanced analytics, and modern data engineering and ML frameworks—primarily using Databricks on Azure/AWS.

Skills & Qualifications

Required

• Bachelor’s degree or higher in Computer Science, Data Science, Mathematics, Statistics, Engineering, or related field.
• 3+ years of experience in data science or machine learning roles.
• Advanced knowledge of Databricks, including:
• PySpark / Spark SQL
• Databricks notebooks
• Delta Lake
• MLflow
• Databricks Jobs & Workflows
• Strong programming skills in Python (pandas, numpy, scikit-learn).
• Experience working with large-scale data processing.

Solid understanding of machine learning algorithms and statistical techniques

Key Responsibilities

Data Science & Machine Learning

• Develop, train, and optimize machine learning and statistical models using Databricks, Python, PySpark, and MLflow.
• Perform exploratory data analysis (EDA) to identify trends, patterns, and insights in large datasets.
• Deploy ML models into production using Databricks MLflow, Delta Live Tables, or other MLOps pipelines.
• Conduct A/B testing, forecasting, segmentation, anomaly detection, or recommendation systems as required by the business.

Data Engineering & Databricks Platform

• Build scalable, high-performance ETL/ELT pipelines using PySpark, SQL, and Databricks workflows.
• Work with Delta Lake to ensure high-quality, reliable, and performant data.
• Optimize cluster usage and job performance within the Databricks environment.
• Collaborate with data engineers to ensure high-quality data availability for modeling.

Business Collaboration

• Translate business problems into analytical solutions and present findings to non-technical stakeholders.
• Partner with product, engineering, and business teams to drive data-informed decisions.
• Communicate complex statistical concepts in a clear and concise manner.