Machine Learning Engineer

Veriday Inc. is seeking a Machine Learning Engineer for a 6-month contract supporting a banking client in Cincinnati, OH. This hybrid role runs 3 days per week on-site, with a focus on designing and operating scalable production machine learning systems from end-to-end MLOps through real-time delivery and governance.

The engagement centers on building reusable ML platforms and production-grade pipelines that support model development, training, validation, deployment, monitoring, and continuous retraining. You will own the full ML lifecycle, including Feature Store and Model Registry capabilities, CI/CD/CT workflows, and the operational controls required for regulated environments.

Responsibilities

• Design, build, and optimize scalable, reusable ML platforms and production-grade systems spanning model development, training, validation, deployment, and monitoring.
• Own 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, leveraging infrastructure-as-code and automated environment provisioning.
• Design and support enterprise Feature Stores and a Model Registry to improve feature reuse, consistency, and governance.
• Partner with data engineering to create scalable training and inference data pipelines integrating 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 monitoring and alerting for model performance, data quality, and drift (data, concept, and feature), plus system health, with incident management and retraining strategies.
• Optimize ML solutions 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.

Requirements

• 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 managing the end-to-end ML lifecycle, including deployment and orchestration, experiment tracking, model versioning, automated retraining, and Responsible AI/model governance.
• Hands-on experience running ML workloads in the cloud (AWS SageMaker, Azure Machine Learning, or Google Vertex AI) for both batch and real-time solutions.
• Strong proficiency in Python and SQL; experience with Java and 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 for large-scale structured and unstructured data.
• Experience building CI/CD pipelines for ML workloads using Git (GitHub/GitLab), 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 (such as Apache Kafka) and real-time inference pipelines.
• Experience with monitoring and observability tools such as Prometheus, Grafana, ELK Stack, or cloud-native monitoring; regulated-industry experience is highly desirable.

Technologies

• 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
• CI/CD/CT pipelines, infrastructure-as-code, streaming technologies, real-time inference, batch inference

Benefits

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

Engagement Details

• Contract type: Contract
• Duration: 6 months, with potential extension
• Employment type: W2 only
• Location: Cincinnati, OH (hybrid, 3 days per week on-site)
• Compensation: USD 70 - 85 per hourly

Preferred Skills

• 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 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).