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Machine Learning Operations Senior Engineer in Madrid

Banco Santander

Workplace
Onsite
Hours
Full-Time
Internship
No
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Job Description

This role represents a critical bridge between data scientists and the rest of the business areas within the company. Ideally you have been a developer for a while, and now you want to transition towards the Data Science world. You don't feel confident enough about algorithm but you know how to overcome that lack of knowledge with your coding skills and quick prototyping attitude. You want to learn about the algorithmic secrets of Data Scientists and you know how to teach about software development best practices. You love open source projects and have contributed to some of them. You find yourself perfectly represented here: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Web_Development_Data_Science_Full.png 
 
Please include your github repo or your stackoverflow profile (or a project you are proud of) in your CV. It is a mandatory requirement in order to consider your application, otherwise it will be discarded. 
 
The MLOps provides expert guidance and delivers through self and others to:  
  1. Develop data-related product-prototypes, creating real-life integrations of AI-based models developed by data scientists.  
  2. Simplified machine learning model deployment. Data scientists use a variety of modeling languages, frameworks, and tools. With MLOps, IT operations teams can quickly deploy machine learning models from a variety of languages and frameworks in production environments. 
  3. Production life cycle management. The initial model deployment is the beginning of a long life cycle of updates to keep a machine learning model running. MLOps provides a means to test and update models in production without interrupting service to business applications. 
  4. Impose software development methodologies and good coding practices for data-scientists.  
  5. Organize and structure data science code to optimize the engineering of data usage. 
  6. Automatizes everything that falls in your hands.  
  7. Eases the access to documentation, insights and knowledge using the most accesible tools, and developing an open source culture.  
Qualifications

Core competencies, knowledge and experience:
  • Experience working with scala, software design patterns and TDD.
  • Experience working with big data (spark is a must, hadoop, hive, kafka).
  • Experience with different database structures, including SQL (postgres, mysql) and NOSQL (cassandra, redis, elastic search).
  • Experience and expertise across data integration and data management with high data volumes.
  • Experience with AWS ecosystem (AWS, EMR, s3, redshift, lambda, glue, athena)
  • Experience working in agile continuos integration/devops paradigm and tool set (git, jenkins, sonar, nexus, jira, splunk)
Must have technical / professional qualifications:
  • Experience with some SQL database structures. Ideally also NoSQL.
  • Experience with web development tools like Javascript, and the integration through/with APIs.
  • Curiosity about predictive modeling, machine learning and data visualization, but no experience is required.
 
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