Responsibilities: Data Pipeline Design and Implementation: Develop and implement data pipelines that ingest, transform, and load data into a format suitable for machine learning models. Data Quality and Management: Ensure data quality through data cleaning, validation, and transformation processes. Model Development and Deployment: Collaborate with data scientists to develop and deploy machine learning models, including building and testing them. Feature Engineering: Create and select relevant features from the data to improve model performance. Model Monitoring and Maintenance: Monitor model performance and identify areas for improvement. Scalability and Performance: Design and build data infrastructure that can handle large volumes of data and support real-time analytics. Collaboration: Work closely with data scientists, software engineers, and other stakeholders to achieve common goals. Data Security and Compliance: Ensure data security and compliance with relevant regulations. Documentation: Maintain comprehensive documentation for data pipelines and models. Key Skills: Programming Languages: Proficiency in Python Generative Ai: LLM Machine Learning Frameworks: Knowledge of frameworks . Data Warehousing: Experience with data warehousing solutions like Redshift or Panoply. Cloud Computing: Familiarity with cloud platforms like AWS, Azure, or GCP. Data Structures and Algorithms: Strong understanding of data structures and algorithms. Statistical Analysis: Ability to perform statistical analysis and interpret results. Communication and Collaboration: Excellent communication and collaboration skills.

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