About usRavenPack is the leading big data analytics provider for financial services. Financial professionals rely on RavenPack for its speed and accuracy in analyzing large amounts of unstructured content. RavenPack's products allow clients to enhance returns, reduce risk and increase efficiency by systematically incorporating the effects of public information in their models or workflows. Our clients include the most successful hedge funds, banks, and asset managers in the world!What are we looking for?We are looking for a MLOps Engineer to join a team responsible for building and maintaining the entire ML infrastructure. Taking offline models and turning them into real production systems will be one of the main activities. In addition, designing and developing comprehensive test and monitoring strategies will also be required. The candidate must have all the technical skills for implementing and maintaining the infrastructure in the cloud. The ideal candidate must have a proven track record in MLOps/DevOps or software automation projects and the ability to understand complex software solutions. The candidate will have experience in all phases of the software development life cycle, from requirements gathering, designing CI/CD pipelines, integration testing, monitoring ML models performance, and supporting production systems. The ability to communicate effectively in English both in writing and verbally is a must. Knowledge of Spanish is not a business requirement. European Union's legal working status is required.About youWe want you to be a software passionate, with a strong technical background. You will be facing awesome challenges by covering all the ML development lifecycle stages and using the latest technologies for it.How will you do it?First month:The onboarding process starts. You will have scheduled meetings with the main stakeholders of the different teams in order to get an overall understanding of all company's products and specifically focused on the projects you will be working on. You will start participating in the scrum ceremonies and also reviewing the actual ML processes and infrastructure.After 3 months:Now you are able to start contributing to the ongoing work related to existing ML infrastructure and start collaborating closely with QAs, ML, and DevOps engineers. Since now you have the overall view of the products and the tech stack, you can propose new initiatives and strategies and start working on them.6 months in:Now you are fully aware of all the company products and ongoing projects and you have the know-how to work with them. You became a key member of the team and you are contributing successfully to the different ML development phases. You are creating comprehensive deployment and monitoring strategies in a CI/CD environment. Also, you are able to provide guidance about good practices and collaborate with different teams.
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