Data and MLOps Engineer

• Work with Data Scientists, Machine Learning experts and Business stakeholders to design cloud-based, highly available Data Infrastructure for AI Workload.
• Design the architecture of and implement cloud-based Data infrastructure / ETL pipelines according to best practices.
• Proactively identify and automate manual data processes
• Implement release processes, workflows, and live deployments in complex IT environments
• Ensuring Post-release application stability and conducting post-mortem of failed routines
• Implement processes for Continuous integration, Test automation and Deployment (CI/CD Pipelines)
• Supporting prototyping activities and driving minimum viable products (MVP)
• Understand and translate business and application specific needs into technical requirements
• Consolidating complex data environments consisting of multiple data sources and formats
• Provide quality documentation of your design (process and workflows) and implementation including experiment tracking / logs.
• Take responsibility of end-to-end development of a module, work independently and where required take initiative to collaborate.

• Academic Degree in Informatics, Computer Science or comparable qualification
• Strong programming experience with Python or CPP.
• Project based practical experience of working with Amazon Web Services (AWS) technologies.
• Practical experience based on previous projects with continuous integration, continuous deployment and test automation in architectures (eg. Jenkins, GitHub Actions).
• Project based hands-on experience of container and DevOps technologies such as Git, Docker, Kubernetes, KubeFlow
• Experience with data analytics tools such as Power BI or Tableau.
• SQL, NoSQL
• Nice to have: Hadoop, Spark, Spark Streaming, Presto, Hive, Crontab, Airflow
• Experience in Agile development methods (Scrum, Kanban)
• Excellent communication skills and capability to present technological concepts coherently to the business and innovation process

Continental