Position Overview

We are seeking a skilled ML Ops Engineer for a short-term contract role (90–120 days) to help build and operationalize scalable machine learning infrastructure in the cloud. This role is critical to enabling enterprise ML solutions that support supply chain planning, design, and execution.

The ideal candidate will have hands-on experience with Databricks, MLflow, PySpark, and Unity Catalog, with a strong foundation in building cloud-native ML pipelines and enforcing data/model governance at scale.

Key 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

Required Qualifications

• 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

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

Compensation & 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