Data and AI/ML Engineer
Vor 4 Tagen
- Design and develop data pipelines that extract data from various sources (e.g., databases, data warehouses, APIs, and external systems), transform data into the desired format, and load it into the appropriate data storage systems focusing on MS Azure services (e.g., Data Factory, Synapse, Databricks)
- Ensure data consistency and integrity during the integration process, performing data quality checks, data validation, and cleaning as needed
- Monitor and tune data systems, identify and resolve performance bottlenecks, and implement caching and indexing strategies to enhance query performance
- Design, develop, and deploy AI/ML solutions, working closely with data scientists to transform data insights into predictive models and intelligent systems ensuring data quality, security, and governance
- Monitor, test, and evaluate the performance of AI/ML models, implementing continuous improvement processes to enhance accuracy and efficiency
- Collaborate with business teams in the areas of R&D/Clinical or Manufacturing/Supply Chain to integrate solutions into business processes, ensuring business impact and operational feasibility in daily activities
- Share knowledge, best practices, and insights in a growing data analytics and engineering community at CureVac
- University degree in computer science, data science, software engineering, or related quantitative field, or an equivalent qualification
- Work experience in designing and managing data pipelines with knowledge about data modeling, ETL processes, and big data technologies (e.g., Spark) to support AI & ML on a public cloud platform, preferably MS Azure
- Experience working with advanced statistics, machine learning, and deep learning including different architectures, embeddings, loss functions, and optimization strategies
- High proficiency in programming languages such as Python, SQL, and DataWeave and libraries/frameworks, e.g., TensorFlow, PyTorch
- Ability to work in diverse, cross-functional teams in a dynamic business environment based on agile development principles
- Capability to describe business use cases/outcomes, data sources and management concepts, and analytical approaches/options also to non-technical audiences
- Fluency in English, German is a plus
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