GAINSystems
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
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