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 If you have the required skills and are ready for an exciting challenge, we encourage you to apply.

Requirements

Must-haves

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
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.
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.

Nice-to-haves

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

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Keywords to mirror

machine learningmlci/cdaiawsazurepythonsqljavadockerkubernetesgcp

Platform: bamboohr
Effort: medium

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