Contract MLOps Engineer — Enterprise ML Platforms

Verida; Cincinnati (OH); Hybrid; USD 83,000 - 152,000; Full time

Benefits offered by this job

Competitive hourly rate

Collaborative environment

Cutting-edge projects with leading-b a

Job summary

Veriday Inc. seeks an experienced Machine Learning Engineer to design, build, and operate scalable ML platforms for a 6-month contract with a reputed banking client in Cincinnati, OH.

You will own the end-to-end ML lifecycle, from feature engineering and model deployment to monitoring, governance, and retraining, and help establish enterprise MLOps capabilities such as a Feature Store and Model Registry. You will collaborate with data scientists, data engineers, software engineers, and business

Qualifications

• 5+ years of experience in ML engineering and MLOps.
• Experience designing end-to-end ML lifecycle management including deployment, tracking, and governance.
• Hands-on cloud experience with AWS/Azure/GCP for ML workloads.
• Proficiency in Python and SQL; Java/Scala a plus.
• Experience with Feature Store and Model Registry implementations.

Responsibilities

• Design, build, and optimize scalable ML platforms and production-grade ML systems.
• Manage end-to-end ML lifecycle including model versioning and retraining.
• Build and maintain CI/CD/CT pipelines for testing, deployment, and monitoring.
• Design enterprise Feature Stores and a Model Registry for governance.
• Collaborate with data scientists, data engineers, software engineers, and stakeholders.

Skills

ML Engineering

MLOps

Cloud platforms

Docker

Kubernetes

CI/CD

GitHub Actions

Terraform

PySpark

Data Engineering

Tools

Docker

Kubernetes

CI/CD tools

GitHub Actions

Terraform

Snowflake

PySpark

Job description

Veriday Inc. seeks an experienced Machine Learning Engineer to design, build, and operate scalable ML platforms for a 6-month contract with a reputed banking client in Cincinnati, OH.

You will own the end-to-end ML lifecycle, from feature engineering and model deployment to monitoring, governance, and retraining, and help establish enterprise MLOps capabilities such as a Feature Store and Model Registry. You will collaborate with data scientists, data engineers, software engineers, and business