ML Engineer
4 weeks ago
Requirements :
- Knowledge of AB testing : The candidate should have a solid understanding of AB testing methodologies, which are commonly used in data-driven decision-making processes to evaluate the effectiveness of different strategies or interventions.
- Causal Inference: Proficiency in causal inference is crucial for understanding cause-and-effect relationships in data and making informed decisions based on observational data.
- Expert in Object-oriented Python programming : Strong expertise in Python programming, particularly in object-oriented programming, is essential for developing machine learning models and algorithms efficiently.
- Pyspark : Experience with PySpark, which is a Python API for Apache Spark, is important for handling large-scale data processing tasks efficiently.
- Databricks : Familiarity with Databricks, a unified analytics platform optimized for Azure, is valuable for managing big data and running collaborative data science projects.
- Cloud deployment on Azure : Proficiency in deploying machine learning models on the Azure cloud platform is necessary for the scalability and accessibility of the developed solutions.
- FAST API : Knowledge of FAST API, a modern web framework for building APIs with Python, is beneficial for creating scalable and high-performance web services to deploy machine learning models.
- Docker : Experience with Docker, a popular containerization platform, is important for packaging machine learning applications into containers for easy deployment and management.
- Git : Proficiency in Git, a version control system, is essential for collaborative software development and tracking changes in code repositories.
- Knowledge of package managers such as PIP and POETRY : Understanding package management tools like PIP and Poetry is crucial for managing dependencies and libraries in Python projects effectively.
(ref:hirist.tech)-
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