About the Team
You will be part of a cross-functional, cross-location team in the AI One Conversation project, which plans to personalize & individualize customer interaction for Deutsche Telekom’s customers across different touchpoints (e.g., Website, Mobile Applications, Call Center, in-shop, TV product, etc.). AI One Conversation is the tool for extensive customer experience with predictive, proactive, and personalized responses to all our customers.
We are working with cutting-edge technology including modern architecture for 360-degree contextualized information about our customers, including what they did on our websites, in the apps, what they talked about with our call center or customer care, etc., to really “know” our customers and be able to better individualize our interaction for them.
What will you do?
- Work closely with product, data science, and software engineering team on the delivery of scalable and real-time machine learning system
- Design, implement, evaluate, deploy, and monitor the machine learning infrastructure
- Enable systems to score hundreds of machine learning models in parallel and real-time
- Setup and build components to automate the machine learning life cycle, including model management, automated model retraining based on continuous model evaluation and deployment
- Design and build components for automatic set up and evaluation of A/B testing experiments
- Build and maintain innovative machine learning model training, scoring & evaluation libraries - for use in production environment
What will you bring along?
- Motivation to build products that delight our customers
- A mindset of continuous learning, willingness to share knowledge and learn from others
- Ability to innovate and find solutions to complex problems
- A perfect balance between being a strong individual contributor and an empathic team player
- Ability to communicate effectively with different stakeholders in the company
What do you need to have?
- Strong hands-on experience in developing, setting up, and maintaining a machine learning infrastructure in cloud and on premise
- Knowledge of components/tools/blueprints to support scalable, resilient and flexible infrastructures supporting data driven functionalities (e.g., based on deep learning)
- Experience working in an environment that supports machine learning modeling and real-time scoring and/or its integration into a product
- Hands on experience in working with distributed systems in a real production setting
- Familiar with standard software engineering methodology, e.g., unit testing, code reviews, design documentation as well as agile working modes and team setups
- Familiar with the best practices of system and online service design - e.g., monitoring and observability, making informed performance trade-offs, maintainability and extensibility, etc.
- Bachelors (or higher) in Machine Learning, Data Science, Computer Science, Statistics, Applied Mathematics or another related field