What are the responsibilities and job description for the Senior MLOps Engineer position at FreightVerify?
FreightVerify is a leading Software-as-a-Service (SaaS) company that is transforming the supply chain and distribution industries with data-driven insights. By leveraging network visibility, we empower businesses with optimizations and recommendations that enhance efficiency and performance. We are seeking an experienced Senior MLOps Engineer to enhance our ML/AI infrastructure and work with our cross-functional teams to deliver scalable, reliable, and efficient machine learning systems.
Job Summary
As a Senior MLOps Engineer, you will be responsible for the end-to-end deployment, scaling, and maintenance of machine learning models, as well as the data pipelines that support them. This hands-on role requires strong expertise in cloud-native MLOps and data engineering, with a focus on automation, observability, and cross-functional collaboration. Your work will help operationalize machine learning at scale, ensuring high reliability and seamless integration within our multi-cloud environment.
Note: This position is located in Ann Arbor, MI and requires onsite work.
Primary Responsibilities:
- Design, deploy, and manage scalable data pipelines for AI/Machine Learning, integrating with tools like Kubernetes, EMR, Databricks, and Luigi.
- Collaborate with data engineering, business analysts, product owners, and other engineering teams to align on requirements and deliver robust ML solutions.
- Build and maintain automated CI/CD pipelines for model deployment, versioning, and monitoring.
- Ensure cross-cloud compatibility and optimize multi-cloud architectures (AWS, Azure).
- Use and manage tools such as Kubeflow, Airflow, and MLflow for pipeline orchestration, experiment tracking, and deployment.
- Implement monitoring and observability tools for model performance, data drift, and reliability using Prometheus, Grafana, and Evidently AI.
- Leverage Kafka for efficient data streaming and real-time processing capabilities within AI/Machine Learning pipelines.
- Conduct regular code and infrastructure reviews, prioritizing maintainability and scalability.
- Stay hands-on with programming, mostly using Python, where appropriate to support Spark-based workflows.
Qualifications
- 5-10 years of experience in MLOps, with a strong background in cloud-native machine learning, data engineering, and automation.
- Proficiency in Python.
- Experience with best-in-class tools for MLOps and data engineering, such as EMR, Databricks, Luigi, Kafka, MLflow, and Airflow.
- Proven experience with CI/CD pipelines and testing frameworks for model and pipeline deployment.
- Hands-on experience with multi-cloud environments, especially AWS and Azure.
- Strong understanding of observability, monitoring, and model drift detection.
- Solid grasp of DevOps principles, with practical experience in production-level model and data pipeline support.
- Excellent problem-solving skills and ability to work effectively in a collaborative environment.
Why Join Us?
- Be part of an innovative team that’s making a real impact on supply chain and logistics optimization.
- Work with cutting-edge MLOps tools and multi-cloud architectures.
- Enjoy growth opportunities within a company that values technical leadership and creative problem-solving.
