Machine Learning Engineer (m/f/d)

As a Machine Learning Engineer at T-Systems, your responsibilities will include designing, implementing, and maintaining AI/ML solutions tailored to diverse business challenges. You'll work closely with cross-functional teams to develop solutions like RAG chatbots and natural language analysis tools, ensuring these applications enhance customer experiences and improve operational efficiency.

To apply for the Machine Learning Engineer role at T-Systems, you should possess strong knowledge of Python, experience with deep learning techniques, and familiarity with RAG system development. It's essential to have a background in cloud-native technologies, containers, and version control systems like Git. Additional skills in NLP techniques and various database technologies are an added advantage.

At T-Systems, Machine Learning Engineers will work with a variety of tools and technologies, including Python, Postgres, Hadoop, and Spark. You'll utilize CI/CD practices for deployment, and also leverage platforms like OpenAI and Kubernetes for implementing and managing your ML solutions.

The work culture at T-Systems is positive, dynamic, and supportive. As a Machine Learning Engineer, you'll enjoy a collaborative environment that values innovation, creativity, and personal growth. With a hybrid work model and flexible schedules, the focus is on achieving meaningful work while enjoying the process.

T-Systems offers a comprehensive benefits package for Machine Learning Engineers, including a flexible compensation plan with medical insurance and transportation allowances, continuous training opportunities, more than 26 vacation days, and access to various specialists. You'll also find a strong commitment to employee well-being with perks like life and accident insurance.

When answering this question, focus on specific frameworks you've used, such as TensorFlow or PyTorch, and describe projects where you applied these frameworks. Highlight any particular challenges you faced and how you overcame them to successfully implement your models.

Discuss what a RAG (Retrieval-Augmented Generation) chatbot is and your hands-on experience in implementing one. Cover the technology stack you utilized, the specific use cases you addressed, and the outcomes of your chatbot deployment.

Explain techniques you use to prevent overfitting, such as regularization methods, cross-validation, and data augmentation. Share a relevant experience where you encountered overfitting and the steps you took to address it effectively.

Provide specific details about an NLP project you've worked on, discussing the techniques used, such as tokenization or sentiment analysis, and the impact your work had on the project's success. Emphasize your collaboration with team members to achieve project goals.

Discuss your approach to deploying machine learning models, including any CI/CD pipeline strategies you've employed. Mention tools you use for monitoring performance and how you ensure models run optimally in production environments.

Share your experience in deploying machine learning models on cloud platforms like AWS or Azure. Focus on how you leveraged these environments to enhance scalability and performance of your projects.

Illustrate your collaborative process, such as conducting regular meetings, understanding their requirements, and merging domain knowledge with technical implementation. Emphasize any tools you use for communication and project management.

Talk about the resources you utilize for keeping abreast of new technologies and methodologies, such as academic journals, conferences, or online courses. Mention any communities or networks you are part of for knowledge sharing.

Prepare a response that connects your career aspirations with T-Systems' mission. Discuss your interest in their innovative projects, company culture, and how you believe your skills will contribute to their success while helping you grow professionally.

Express your understanding of the ethical implications and responsibility in developing AI/ML solutions. Discuss how you prioritize fairness and transparency in your work and your approach to addressing potential biases.