Used Tools & Technologies
Not specified
Required Skills & Competences ?
Docker @ 3 Go @ 3 Kafka @ 3 Kubernetes @ 3 Linux @ 3 DevOps @ 3 Python @ 3 SQL @ 3 Spark @ 3 GCP @ 3 ETL @ 3 Airflow @ 3 NoSQL @ 3 RDBMS @ 3 CI/CD @ 3 Distributed Systems @ 3 Machine Learning @ 3 MLOps @ 3 Bash @ 3 MLFlow @ 3 Agile @ 3Details
Your passion is to work with the latest and greatest technologies in the field of Machine Learning Engineering. You will work as a machine learning platform engineer on different projects within the Analytics Engineering, helping data scientists train, deploy, monitor and productionize models. You will take part in developing and maintaining a data execution platform, keep an eye on good coding practices and create re-usable code.
Responsibilities
- Familiar with software engineering practices like versioning, testing, documentation, code review.
- Knowledge of MLOps architecture and practices.
- Programming in Python or Go.
- Experience with Apache Airflow and MLflow.
- Experience in setting up both SQL as well as noSQL databases.
- Knowledge of DevOps methodology and tooling.
- Experience with monitoring, alerting and observability.
- Experience in Kubernetes (deployments and managing Kubernetes applications).
- Experience in building data-oriented platforms.
- Relevant work experience in ML or data projects.
- Deployment and provisioning automation tools e.g. Docker, CI/CD.
Nice to have
- Experience with distributed systems and clusters for both batch as well as streaming data (S3/Spark/Kafka).
- Hands-on experience building complex data pipelines e.g. ETL.
- Basic knowledge of Machine Learning/AI and hands-on technologies and frameworks used in ML.
- Bash scripting and Linux systems administration.
- Experience with building distributed, large scale and secure applications.
- Experience working in cloud environment (e.g. GCP).
- Good understanding of databases including RDBMS.
- Experience with working in an agile/scrum way.
- Being a committer to Open Source projects is a strong plus.