AI Platform & MLOps Engineer — Scale ML in Production

Greymatter Innovationz; New York (NY); On-site; USD 120,000 - 150,000; Full time

Benefits offered by this job

Motivating Work Environment

Excellent Work Culture

Career development

Job summary

Greymatter Innovationz in NYC is seeking an AI Platform / ML Engineer to build, operationalize, and scale ML solutions. You will develop reusable workflows, integrate data sources, and ensure reliable model deployment and lifecycle management across platforms like Dataiku and Databricks.

The role emphasizes MLOps, collaboration with data scientists and engineers, and delivering production-ready AI capabilities in a fast-paced, cross-functional team.

Qualifications

• 3–6 years of experience in MLOps, machine learning engineering, or AI model deployment.
• Hands-on experience with platforms like Dataiku or Databricks for building and managing ML workflows.
• Strong programming in Python, familiarity with SQL, and experience with containerization/CI-CD pipelines for AI models.
• Strong collaboration skills and ability to work in cross-functional teams.

Responsibilities

• Model Operationalization (MLOps): Build, test, and deploy AI and ML solutions into production environments using Dataiku, Databricks, or similar platforms.
• Workflow Development: Create reusable workflows and integrate ML pipelines with core data sources.
• Monitoring & Maintenance: Monitor model performance, drift, and production operations; support continuous improvement.
• Team Collaboration: Work with data scientists, data engineers, and business analysts to bridge experiments and enterprise apps.

Skills

MLOps

Python

SQL

CI/CD pipelines

Cross-functional collaboration

Tools

Dataiku

Databricks

Docker

Job description

Greymatter Innovationz in NYC is seeking an AI Platform / ML Engineer to build, operationalize, and scale ML solutions. You will develop reusable workflows, integrate data sources, and ensure reliable model deployment and lifecycle management across platforms like Dataiku and Databricks.

The role emphasizes MLOps, collaboration with data scientists and engineers, and delivering production-ready AI capabilities in a fast-paced, cross-functional team.