MLOps Engineer: GCP, Airflow & CI/CD Expert

Jobtailor; Alpharetta (GA); On-site; USD 120,000 - 170,000; Full time

Job summary

Jobtailor in Alpharetta, GA is seeking an experienced MLOps Engineer to manage the ML lifecycle and automate data workflows. The role requires strong Python skills for data processing, SQL querying, and hands-on GCP experience, including Cloud Composer for Airflow-based orchestration.

You will design and implement end-to-end ML pipelines, monitor pipelines, and contribute to CI/CD for ML models using Docker and Kubernetes in a cloud-native stack.

Qualifications

• Strong experience in MLOps practices and machine learning lifecycle management.
• Proficiency in Python for data processing, automation, and ML workflows.
• Strong knowledge of SQL and database querying.
• Hands-on experience with Google Cloud Platform (GCP).
• Experience with Cloud Composer (Apache Airflow on GCP) for workflow orchestration.

Responsibilities

• Apply MLOps practices and manage the machine learning lifecycle
• Perform data processing, automation, and ML workflows using Python
• Query and work with databases using SQL
• Use Google Cloud Platform (GCP) and Cloud Composer for workflow orchestration
• Schedule and manage data and ML pipelines with Apache Airflow
• Perform data transformation and analytics engineering with Dataform
• Automate, monitor, and orchestrate workflows and pipelines
• Support CI/CD pipelines and ML model deployment
• Work with containerization technologies such as Docker and Kubernetes

Skills

MLOps Practices

Python Programming

SQL Database Querying

Tools

Google Cloud Platform (GCP)

Cloud Composer

Apache Airflow

Dataform

Docker

Kubernetes

Job description

Jobtailor in Alpharetta, GA is seeking an experienced MLOps Engineer to manage the ML lifecycle and automate data workflows. The role requires strong Python skills for data processing, SQL querying, and hands-on GCP experience, including Cloud Composer for Airflow-based orchestration.

You will design and implement end-to-end ML pipelines, monitor pipelines, and contribute to CI/CD for ML models using Docker and Kubernetes in a cloud-native stack.