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Machine Learning Engineer

Veriday Inc.
Cincinnati, USA
Mid · 5+ years experience
Hybrid
Discovered 2 days ago
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Full Job Posting

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

Qualifications

  • 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 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.

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