ML Engineer
1 week ago
Role & responsibilities
- Design and manage end-to-end ML pipelines for deployment and monitoring.
- Automate model training, retraining, and deployment workflows.
- Implement CI/CD for ML models to ensure reliability and scalability.
- Build real-time dashboards and APIs for model outputs and business consumption.
- Optimize cloud resources (Azure Databricks, ADF, Azure ML) for performance and cost efficiency.
- End-to-end model lifecycle management from development to deployment.
- Collaborate with data scientists, engineers, and business teams to operationalize ML solutions.
Preferred candidate profile
- 3+ years of experience in MLOps / Data Science and model deployment.
- Strong skills in Python, PySpark, SQL and automation frameworks.
- Hands-on experience with Azure ML, Databricks, Azure Data Factory.
- Knowledge of containerization (Docker, Kubernetes) and model serving frameworks (MLflow, FastAPI, Flask).
- Retail or e-commerce industry experience required.
- Experience in end-to-end model development and deployment.
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