Machine Learning Engineer

Veriday Inc.•Lockland, OH

4d•$70 - $85•Hybrid

About The Position

Veriday Inc. is seeking an experienced Machine Learning Engineer for a six-month contract assignment with a banking client in Cincinnati, Ohio, with potential for extension. The position is hybrid, with three days per week onsite, and is W2 only. The engineer will design, build, and operate scalable machine learning platforms and production ML systems, owning the end-to-end ML lifecycle from feature engineering and model deployment through monitoring, governance, and continuous retraining. The role includes establishing enterprise MLOps capabilities such as a Feature Store, Model Registry, automated CI/CD/CT pipelines, and real-time scoring. The engineer will work closely with data scientists, data engineers, software engineers, product owners, and business stakeholders, preferably in regulated industries.

Requirements

At least 5 years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering, including at least 3 years building and supporting production ML systems.
Demonstrated experience with end-to-end ML lifecycle management, including deployment and orchestration, experiment tracking, model versioning, automated retraining, and Responsible AI/model governance.
Hands-on experience operating cloud ML workloads using AWS SageMaker, Azure Machine Learning, or Google Vertex AI, supporting batch and real-time solutions.
Strong proficiency in Python and SQL; Java and Scala are also listed.
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 tools.
Experience with Docker and Kubernetes; OpenShift is preferred; 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, the ELK Stack, or cloud-native monitoring.
Contract assignment is W2 only.

Nice To Haves

Experience in a regulated industry such as banking, financial services, healthcare, insurance, or telecom.
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 AWS, Azure, or GCP cloud certifications.
Experience with OpenShift.

Responsibilities

Design, build, and optimize scalable, reusable ML platforms and production-grade ML systems covering 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 integrating data warehouses, data lakes, and operational sources, with 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, including 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; mentor junior engineers and drive MLOps standards and best practices.