Remote Databricks MLOps Engineer: Scale ML Pipelines

KData Inc.; United States; Remote; USD 120,000 - 160,000; Full time

Benefits offered by this job

Remote work

Job summary

KData Inc. is seeking a highly skilled MLOps Engineer to join our data team for a critical 6-month initiative.

The role focuses on automating, scaling, and managing the end-to-end lifecycle of machine learning models within the Databricks ecosystem in a fully remote setting. You will bridge Data Science and Data Engineering, build robust CI/CD pipelines, manage experiment tracking with MLflow and Unity Catalog, optimize Databricks clusters for cost and performance, and collaborate with PoCs to

Qualifications

• Bachelor's degree in CS, Data Science, Engineering, or related field.
• 6+ years in Software Engineering, Data Engineering, or DevOps with 3+ years in MLOps.
• Strong Python/SQL and PySpark skills.
• Hands-on Databricks workflows (MLflow, Unity Catalog, Delta Lake).

Responsibilities

• Design, build, and maintain CI/CD and MLOps pipelines for training, evaluation, deployment, and scoring.
• Implement experiment tracking, model registration, versioning, and environment promotion policies.
• Optimize Databricks clusters for cost and performance.
• Collaborate with data engineers on scalable feature pipelines (Databricks Feature Store / Delta Lake).
• Establish monitoring for model performance, data drift, and system health.
• Partner with Data Scientists to productionize PoC code into ML products.

Skills

Python

SQL

PySpark

MLOps

Education

Bachelor's degree in Computer Science or related field

Tools

Databricks

MLflow

Unity Catalog

Delta Lake

Databricks Repos

Terraform

Docker

Kubernetes

Job description

KData Inc. is seeking a highly skilled MLOps Engineer to join our data team for a critical 6-month initiative.

The role focuses on automating, scaling, and managing the end-to-end lifecycle of machine learning models within the Databricks ecosystem in a fully remote setting. You will bridge Data Science and Data Engineering, build robust CI/CD pipelines, manage experiment tracking with MLflow and Unity Catalog, optimize Databricks clusters for cost and performance, and collaborate with PoCs to