Machine Learning Engineer

Build and operate production machine learning systems for a 6-month contract with a banking client in Cincinnati, OH (hybrid, 3 days/week on-site). Veriday Inc. supports a collaborative, fast-moving environment and offers a competitive hourly rate (USD 70–85/hour). If you enjoy taking models from end-to-end lifecycle ownership to reliable, monitored deployment, this role brings the focus to real-world impact.

What you’ll do

• Design, build, and optimize scalable, reusable ML platforms that cover the full path from development and training through validation, deployment, and monitoring.
• Own the end-to-end ML lifecycle including model versioning, experiment tracking, reproducibility, and governance.
• Create and maintain automated CI/CD/CT pipelines for model testing, validation, deployment, rollback, and retraining, using infrastructure-as-code and automated environment provisioning.
• Develop 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.
• Implement frameworks for batch and real-time inference, including low-latency, event-driven scoring built on streaming technologies.
• Set up monitoring and alerting for performance, data quality, drift (data, concept, feature), and system health, including incident management and retraining strategies.
• Optimize for performance, reliability, maintainability, and cost efficiency across ML systems.
• 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, while mentoring junior engineers and driving MLOps best practices.

What you bring

• 5+ years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering, with 3+ years building and supporting production ML systems.
• Proven capability 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 Python and SQL skills; Java and/or Scala is a plus.
• 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 ML CI/CD pipelines using Git/GitHub/GitLab and Jenkins, GitHub Actions, Azure DevOps (or equivalent), plus Terraform or other infrastructure-as-code tooling.
• Working knowledge of Docker and Kubernetes (OpenShift preferred), along with 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, and the ELK Stack, or cloud-native monitoring. Experience in regulated industries is highly desirable.

Engagement details

• Employment type: W2 only
• Contract duration: 6 months, with potential extension
• Location: Cincinnati, OH (hybrid, 3 days/week on-site)

Technologies you may work with

Python, SQL, Java, Scala, AWS SageMaker, Azure Machine Learning, Google Vertex AI, Feature Store, Model Registry, Snowflake, Databricks, IBM DB2, dbt, Spark, PySpark, Pandas, Git, GitHub, GitLab, Jenkins, GitHub Actions, Azure DevOps, Terraform, Docker, Kubernetes, OpenShift, Linux, shell scripting, Apache Kafka, Prometheus, Grafana, ELK Stack, and CI/CD/CT pipelines across batch and real-time inference using infrastructure-as-code and streaming technologies.

Nice to have

• Experience implementing enterprise Feature Stores, and designing Model Registry and ML governance frameworks.
• Experience building real-time decisioning or scoring platforms for credit risk, fraud, customer analytics, or recommendation systems.
• Familiarity with platforms such as MLflow, Kubeflow, Airflow, Argo Workflows, Feast, and SageMaker Pipelines (or similar).
• 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).

Why this role

• Competitive hourly rate
• Collaborative and dynamic work environment
• Opportunity to work on cutting-edge projects with a leading banking client