MLOps Engineer: Databricks & ML Production

Darwill; Northern (KY); Hybrid; USD 120,000 - 160,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Darwill is seeking a mid-level Machine Learning Engineer (MLOps) to productionize traditional ML models and to build the core data pipelines on Databricks. This role is designed for independence and hands-on execution while offering growth opportunities.

Based in the Chicago area with a hybrid model requiring 1–2 days onsite weekly, you will collaborate with Data Scientists to deploy propensity and segmentation models, optimize pipelines for scale, and implement monitoring and retraining

Qualifications

• 3–6 years of professional experience in machine learning engineering, data engineering, or a closely related role.
• Experience with production ML and data pipelines in a cloud environment.
• Ability to translate models into production systems in collaboration with data scientists.

Responsibilities

• Design, build, and maintain ETL pipelines in Databricks using Spark and Delta Lake.
• Deploy and monitor ML models (propensity, segmentation) in production.
• Build repeatable ML pipelines for training, scoring, and inference.

Skills

Apache Spark

Databricks

SQL

Python (pandas)

ML concepts

ML workflow concepts

Education

Bachelor's degree in CS or related field

Master's degree preferred

Tools

MLflow

Docker

AWS

Databricks Workflows

Job description

Darwill is seeking a mid-level Machine Learning Engineer (MLOps) to productionize traditional ML models and to build the core data pipelines on Databricks. This role is designed for independence and hands-on execution while offering growth opportunities.

Based in the Chicago area with a hybrid model requiring 1–2 days onsite weekly, you will collaborate with Data Scientists to deploy propensity and segmentation models, optimize pipelines for scale, and implement monitoring and retraining