Machine Learning Ops Engineer

Summary

Join our team as an ML Ops Engineer for a short-term contract (90-120 days) to build and operationalize scalable machine learning infrastructure in the cloud. This crucial role supports enterprise ML solutions for supply chain planning, design, and execution. The ideal candidate possesses hands-on experience with Databricks, MLflow, PySpark, and Unity Catalog, and a strong foundation in cloud-native ML pipelines and data/model governance. You will design and implement scalable ML pipelines, build CI/CD workflows, and ensure production readiness of ML code. Collaboration with cross-functional teams and documentation of technical solutions are also key aspects of this role. The position offers a competitive rate, flexible work hours, and the opportunity to contribute to mission-critical ML initiatives within a high-impact supply chain environment.

Requirements

• Proficient in Python
• Strong experience with Databricks, MLflow, and PySpark for distributed data processing and ML lifecycle management
• Familiarity with Unity Catalog for data security and governance in Databricks
• Experience using Terraform or similar infrastructure-as-code (IaC) tools for provisioning and managing cloud infrastructure
• Experience deploying ML pipelines in cloud platforms (Azure)
• Hands-on with Docker and Kubernetes for containerization and orchestration
• Familiarity with ML frameworks like scikit-learn, TensorFlow, Keras, or PyTorch
• Solid understanding of DevOps, CI/CD practices, and test automation in data science environments
• Excellent collaboration and communication skills

Responsibilities

• Design and implement scalable ML pipelines on cloud platforms (Azure)
• Use Databricks, PySpark, and MLflow to build and manage the ML lifecycle, including training, tracking, and deployment
• Apply Unity Catalog to enforce data and model governance across environments
• Build and maintain CI/CD workflows with GitHub Actions, GitLab CI, or similar tools; integrate orchestration tools like Airflow
• Refactor ML code for production readiness; containerize and deploy models using Docker/Kubernetes
• Automate testing, validation, and monitoring for production models
• Work closely with cross-functional teams to align deployments with business goals in the supply chain domain
• Document technical solutions and ensure knowledge transfer to internal teams

Preferred Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, or a related field
• Cloud certification (Azure)
• Experience with additional ML Ops frameworks (e.g., Kubeflow, DataRobot)
• Background in supply chain planning, design, or execution, with ML applications in demand forecasting, inventory optimization, or logistics
• Familiarity with enterprise supply chain systems

Benefits

• Competitive rate based on experience
• Flexible work hours with remote or hybrid flexibility
• Work on mission-critical ML initiatives in a high-impact supply chain environment
• Collaborate with an experienced, agile team using modern ML Ops tooling