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# Machine Learning Engineer

- **Company:** VERIDAY INC.
- **Location:** Arlington Heights, OH, United States
- **Experience:** Experienced
- **Salary:** $145,600.0 - $176,800.0
- **Contract:** Temporary contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Cloud Computing, Continuous Integration, Information Engineering, Data Warehousing, IBM DB2, Relational Databases, Distributed Systems, Github, Monitoring of Systems, Python (Programming Language), Linux System Administration, Machine Learning, OpenShift, Software Architecture, Recommender Systems, Prometheus, Azure Machine Learning, Shell Script, Software Engineering, SQL Databases, Data Streaming, Unstructured Data, Feature Store, Google Cloud, Feature Engineering, Snowflake, Grafana, Apache Spark, Google Vertex AI, Gitlab, Pandas, Event Driven Architecture, Data Lakes, Pyspark, Kubernetes, Infrastructure Automation Frameworks, Real-time Inference, Low Latency, Apache Kafka, Data Management, Machine Learning Operations, Model Registry, Terraform, Software Version Control, Data Pipelines, Docker, Elk Stack, Jenkins, Databricks
- **Published:** October 22, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=5108472ee9e586c4

## About the Role

* 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).

## Description

* 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.