Machine Learning Academy

Raiffeisen Bank Machine Learning Academy🎓 is our newest trainee program where you will be able to pick-up the needed practical skills that will push your career ahead and give you a head start in the following three roles 👉 Data Scientist, Machine Learning Engineer and ML DevOps.

For 12 months 📆 we will offer you a personalized learning path that will support you in building a solid foundation, where you will benefit from both internal and external trainings & learning resources. You will be mentored by highly experienced Machine Learning Engineers/ML DevOps and you will get to work together with them on our use cases to get some practical experience under your belt.

During the Machine Learning Academy, as a Data Scientist/Machine Learning Engineer/ML DevOps, you will build up the following skills: 👇

AI/ML skills ‍🤖

1. Basic understanding and application of generative models and LLMs
2. Understanding of advanced machine learning and AI methods, including neural networks, NLP, computer vision, and reinforcement learning.
3. Design agentic systems using frameworks such as AutoGen, Swarm
4. Data preparation skills such as cleansing, feature selection and engineering
5. Model benchmarking and evaluation techniques
6. Ability to understand, interpret and explain experiment results
7. Use prompt engineering techniques to enhance application functionality

Software Engineering skills 💻

1. Stay current with the latest Python libraries and technologies for both back-end and front-end development using Django REST framework, Vue.js, Vuetify, and Plotly
2. Design and implementation of application pipelines ensuring efficient delivery of software products
3. Manage databases with Postgres and implement queueing systems with RabbitMQ
4. Utilize Streamlit for building proof-of-concept applications
5. Collaborate on projects and manage version control using Git, with additional skills in related programming languages and tools being a plus

Data skills 📊📈

1. Understanding of the end-to-end flow of data through the architecture, from source systems to consumers, to create efficient and reliable data pipelines.
2. Apache Airflow for creating, scheduling, and monitoring data workflows
3. Distributed computing technologies (PySpark, Impala, Hive) for processing large and complex datasets
4. Building real-time streaming data pipelines in Kafka
5. SQL for modifying, and querying databases
6. Data cataloging, quality, and governance best practices
7. Familiarity with vector databases, such as Redis and Neo4J

DevOps/MLOps skills 🛠️ 🚀

1. Familiarity with Docker for creating, deploying, and managing containerized applications and Kubernetes for automating deployment and scaling
2. Usage of MLflow for tracking experiments, managing model lifecycles, and facilitating reproducibility in machine learning projects
3. Proficiency in managing data storage solutions such as Amazon S3
4. Skills in working with Machine Learning infrastructure on major Cloud providers (eg. AWS, Azure)
5. Deploying machine learning models using SageMaker/Databricks
6. Implementation of CI/CD pipelines to automate testing, building and deployment processes
7. Linux operating system, including system monitoring, process control, as well as understanding of system permissions and security configurations

Make the first step in your career by applying for Raiffeisen Bank Machine Learning Academy!