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.