Machine learning Engineer

ML System Design & Deployment

• Build and deploy end-to-end ML pipelines (training → validation → deployment → monitoring)
• Convert notebooks and prototypes into production-grade services
• Design batch and real-time inference systems

MLOps & Infrastructure

• Implement CI/CD pipelines for ML workflows.

Work with tools like:

• MLflow / Weights & Biases
• Airflow / Prefect
• Docker / Kubernetes
• Manage model versioning, reproducibility, and experiment tracking

Data Pipeline Integration

Collaborate with data engineering teams to

• Build feature pipelines

• Ensure data quality and consistency

• Work with structured and unstructured data

Model Performance & Monitoring 

Set up monitoring for:

• Data drift

• Model drift

• Latency and system failures

• Define SLAs for model performance

Optimization & Scaling

Optimize models for: 

• Latency

• Cost

• Throughput

• Work on inference optimization techniques (quantization, batching, caching)

About Logile

Logile is the leading retail labor planning, workforce management, inventory management and store execution provider deployed in thousands of retail locations across North America, Europe, Australia, and Oceania.  

Our proven AI, machine-learning technology and industrial engineering accelerate ROI and enable operational excellence with improved performance and empowered employees. Retailers worldwide rely on Logile solutions to boost profitability and competitive advantage by delivering the best service and products at optimal cost.  

From labor standards development and modeling to unified forecasting, storewide scheduling, and time and attendance, to inventory management, task management, food safety, and employee self-service — we transform retail operations with a unified store-level solution. Gain the Advantage with The Logic of Retail. One Platform for store planning, scheduling and execution.  

For more information, visit www.logile.com