Senior ML/AI Engineer with GCP

DataArt; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

DataArt in the United States is seeking an experienced engineer to build and operate generative AI and ML products in production, blending ML engineering with backend services, cloud infrastructure, and platform operations.

You will collaborate with domain and platform teams to deliver AI capabilities, support adoption, and improve the reliability and operability of deployed systems while maintaining engineering standards across pipelines and deployments.

Qualifications

• Experience with Python for ML/AI backend services.
• Strong backend engineering and API development.
• Knowledge of CI/CD and software practises.
• Familiarity with observability and production operations.
• Experience with MLOps tools and deployment pipelines.

Responsibilities

• Build and operate generative AI and ML products in production.
• Collaborate with domain and platform teams to deliver AI capabilities.
• Develop and maintain scalable AI models and APIs.
• Manage infra for AI products including observability and reliability.
• Contribute to CI/CD pipelines and infrastructure as code.
• Work with DevOps and data engineering teams to uphold standards.

Skills

Python programming

Backend engineering

CI/CD concepts

Observability & monitoring

MLOps familiarity

Tools

Terraform

Docker

Airflow

MLflow

APIs development

Job description

Role overview

Build and operate generative AI and machine learning products in production, combining ML engineering with backend software, cloud infrastructure, and platform operations. You will work with domain and platform teams to deliver solutions, support adoption of shared AI capabilities, and improve the reliability and operability of deployed systems.

Responsibilities

• Develop AI and data products with domain teams, and help teams adopt shared data and AI platform capabilities.
• Build, deploy, and operate generative AI and ML models in scalable production environments.
• Manage infrastructure for AI products and environments, including observability, performance, and reliability.
• Contribute to CI/CD pipelines, infrastructure as code, and platform automation.
• Collaborate with software, data engineering, MLOps, and DevOps teams to maintain engineering standards.
• Support experimentation frameworks and internal tools for model development and evaluation.

Requirements

• Experience working with Google Cloud Platform.
• Strong Python skills and experience with Terraform or another infrastructure as code tool.
• Backend engineering experience building APIs and services.
• Familiarity with generative AI, machine learning, or MLOps tools and practices, such as Airflow, MLflow, pipelines, and monitoring.
• Understanding of CI/CD, Docker, and software engineering practices.
• Experience or familiarity with model deployment, serving, and production operations.
• Experience with observability, incident response, or platform operations.