Senior MLOps Platform Engineer: Cloud Pipelines & AI

EPAM Systems; Colombia; Remote; COP 381,934,000 - 572,902,000; Full time

Benefits offered by this job

Healthcare benefits

Paid time off

Learning and certification resources

Global career opportunities

Job summary

EPAM Systems seeks a Senior MLOps Platform Engineer to collaborate with a leading real estate data analytics provider. You will migrate systems to the cloud, optimize costs, and enhance availability and reliability through architectural upgrades.

Responsibilities include end-to-end ML lifecycle delivery, translating business goals into ML problems, building data solutions, automating pipelines, and deploying and monitoring models in production.

Qualifications

• 3+ years working as a Cloud Infrastructure Engineer or in a comparable position.
• Strong command of Python.
• Deep knowledge of Google Vertex AI and Vertex AI Pipelines.
• Experience working with Terraform and Jenkins.
• Prior exposure to ML model monitoring.
• GCP knowledge is a plus.
• B2-level English or above.

Responsibilities

• Migrate systems to the cloud to optimize costs and improve availability and reliability.
• Translate business goals into well-defined ML problems.
• Create data solutions that align with business and technical needs.
• Manage data preparation and processing to prepare models for training.
• Lead model training and experimentation initiatives.
• Construct and automate ML pipelines.
• Launch models into production environments.
• Track, tune, and sustain models after deployment.

Skills

Python

English (B2+)

Cloud infrastructure

Tools

Google Vertex AI

Vertex AI Pipelines

Terraform

Jenkins

Job description

EPAM Systems seeks a Senior MLOps Platform Engineer to collaborate with a leading real estate data analytics provider. You will migrate systems to the cloud, optimize costs, and enhance availability and reliability through architectural upgrades.

Responsibilities include end-to-end ML lifecycle delivery, translating business goals into ML problems, building data solutions, automating pipelines, and deploying and monitoring models in production.