Machine Learning Engineer

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. is seeking an experienced Machine Learning Engineer to design, build, and operate scalable machine learning platforms and production ML systems for a 6-month contract assignment 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 continuous retraining. You will also help establish enterprise MLOps capabilities such as a Feature Store, Model Registry, automated CI/CD/CT pipelines, and real-time scoring. You will work closely with data scientists, data engineers, software engineers, and business stakeholders, ideally in a regulated industry such as banking, financial services, healthcare, insurance, or telecom.

• Engagement: Contract assignment with a reputed banking client


• Location: Cincinnati, OH (hybrid, 3 days per week on-site)


• Contract Duration: 6 months, with potential extension


• Employment Type: W2 only

Responsibilities

• Design, build, and optimize scalable, reusable ML platforms and production-grade ML systems that cover model development, training, validation, deployment, and monitoring.


• Manage the end-to-end ML lifecycle, including model versioning, experiment tracking, reproducibility, and governance.


• Build and maintain automated CI/CD/CT pipelines for model testing, validation, deployment, rollback, and retraining, using infrastructure-as-code and automated environment provisioning.


• Design and support enterprise Feature Stores and a Model Registry to improve feature reuse, consistency, and model governance.


• Partner with data engineering to build scalable training and inference data pipelines that integrate with data warehouses, data lakes, and operational sources, with strong data quality, lineage, and observability.


• Develop frameworks for batch and real-time inference, including low-latency, event-driven scoring platforms built on streaming technologies.


• Implement model monitoring and alerting for performance, data quality, data/concept/feature drift, and system health, with incident management and retraining strategies.


• Optimize ML systems for performance, reliability, maintainability, and cost efficiency.


• Implement MLOps governance and ensure compliance with security, privacy, risk, and regulatory requirements, including auditability, explainability, and documentation.


• Collaborate with data scientists, product owners, and engineers to translate business needs into technical solutions, and mentor junior engineers while driving MLOps standards and best practices.

Required Skills

• 5+ years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering, including 3+ years building and supporting production ML systems.


• Demonstrated experience implementing end-to-end ML lifecycle management: deployment and orchestration, experiment tracking, model versioning, automated retraining, and Responsible AI/model governance.


• Hands-on experience operating ML workloads in the cloud (AWS SageMaker, Azure Machine Learning, or Google Vertex AI), supporting both batch and real-time solutions.


• Strong proficiency in Python and SQL; Java, Scala.


• Experience with Feature Store architecture and Model Registry implementation.


• Experience with data platforms and transformation tools such as Snowflake, Databricks, IBM DB2 (or equivalent RDBMS), data lakes, dbt, Spark, PySpark, and Pandas, working with large-scale structured and unstructured data.


• Experience building CI/CD pipelines for ML workloads using Git/GitHub/GitLab and Jenkins, GitHub Actions, Azure DevOps, or equivalent, plus Terraform or other infrastructure-as-code tools.


• Experience with Docker and Kubernetes (OpenShift preferred), plus Linux administration and shell scripting.


• Experience with streaming and event-driven architectures (Apache Kafka) and real-time inference pipelines.


• Experience with monitoring and observability tools such as Prometheus, Grafana, the ELK Stack, or cloud-native monitoring. Experience in regulated industries is highly desirable.

Preferred Skills

• Experience implementing enterprise Feature Stores and designing Model Registry and ML governance frameworks.


• Experience building real-time decisioning or scoring platforms for use cases such as credit risk, fraud, customer analytics, or recommendation systems.


• Familiarity with MLflow, Kubeflow, Airflow, Argo Workflows, Feast, SageMaker Pipelines, or similar platforms.


• Knowledge of distributed systems and software architecture patterns.


• Familiarity with AI/ML regulatory frameworks and model risk management practices.


• Relevant cloud certifications (AWS, Azure, GCP).

What We Offer

• Competitive hourly rate


• A collaborative and dynamic work environment


• The opportunity to work on cutting-edge projects with a leading banking client

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