Machine Learning Engineer

Veriday Inc. is hiring a Machine Learning Engineer for a 6-month contract with a reputed banking client in Cincinnati, OH (hybrid, 3 days/week on-site). This role is built for engineers who want to own production-grade machine learning systems end to end, with the chance to work on cutting-edge projects in a collaborative environment.

You will design, build, and operate scalable ML platforms that support model development, training, validation, deployment, and monitoring. The engagement includes strong MLOps capabilities such as Feature Store, Model Registry, CI/CD and continuous test (CT) pipelines, real-time scoring, monitoring, governance, and continuous retraining.

Responsibilities

• Design, build, and optimize scalable, reusable ML platforms and production-grade ML systems covering the full lifecycle: development, training, validation, deployment, and monitoring.
• Own end-to-end ML lifecycle management, including model versioning, experiment tracking, reproducibility, and governance.
• Develop 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 Model Registry capabilities to improve feature reuse, consistency, and model governance.
• Partner with data engineering to build scalable training and inference data pipelines integrating with data warehouses, data lakes, and operational sources, with strong data quality, lineage, and observability.
• Create frameworks for batch and real-time inference, including low-latency, event-driven scoring platforms using streaming technologies.
• Implement model monitoring and alerting for performance, data quality, and drift (data, concept, feature), including incident management and retraining strategies.
• Optimize ML systems for performance, reliability, maintainability, and cost efficiency.
• Implement MLOps governance and help 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.
• Proven experience managing 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 (choose one or more: AWS SageMaker, Azure Machine Learning, Google Vertex AI), supporting both batch and real-time solutions.
• Strong proficiency in Python and SQL (Java and Scala experience required).
• 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 across large-scale structured and unstructured data.
• Experience building ML CI/CD pipelines using tools such as Git/GitHub/GitLab and Jenkins, GitHub Actions, Azure DevOps (or equivalent), plus Terraform or other infrastructure-as-code tooling.
• 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 including Prometheus, Grafana, the ELK Stack, or cloud-native monitoring; experience in regulated industries is highly desirable.

Benefits

• Competitive hourly rate (USD 70–85/hour)
• A collaborative and dynamic work environment
• The opportunity to work on cutting-edge projects with a leading banking client

Engagement Details

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

Preferred skills: enterprise Feature Stores and Model Registry implementation and ML governance frameworks; experience with real-time decisioning or scoring for use cases such as credit risk, fraud, customer analytics, or recommendation systems; familiarity with ML platforms such as MLflow, Kubeflow, Airflow, Argo Workflows, Feast, or SageMaker Pipelines; knowledge of distributed systems and software architecture patterns; familiarity with AI/ML regulatory frameworks and model risk management practices; and relevant cloud certifications (AWS, Azure, GCP).

Key technologies include: 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, and real-time and batch inference.