Senior AI Engineer (REF3438D)

As a Senior AI Engineer at Deutsche Telekom IT Solutions, your responsibilities will include developing exciting AI solutions for various clients, building and maintaining MLOps pipelines, managing data, and ensuring seamless deployment and monitoring of AI systems. You'll collaborate closely with German colleagues and project members while utilizing modern tools, making creativity and innovation part of your daily work.

To qualify as a Senior AI Engineer at Deutsche Telekom IT Solutions, you should have several years of experience in scalable AI application development, familiarity with modern technologies such as Python, LLMs, and Azure AI services. While not a strict requirement, a background in data science will be advantageous, especially in areas such as computer vision and predictive maintenance.

At Deutsche Telekom IT Solutions, as a Senior AI Engineer, you will work with a range of advanced technologies including Python, Azure AI services, and LLMs. You'll also be proficient in using collaboration tools such as MS Teams to stay connected with your team and efficiently handle projects across different sites.

The work culture for a Senior AI Engineer at Deutsche Telekom IT Solutions is dynamic and innovative. You will be part of a collaborative environment where teamwork and communication flourish, whether you are working remotely from one of our locations in Hungary or engaging with colleagues in Germany, ensuring that creativity and knowledge-sharing are at the forefront.

Yes, remote work is an option for the Senior AI Engineer position at Deutsche Telekom IT Solutions. However, please note that this opportunity is limited to individuals residing within Hungary due to European taxation regulations. Embrace flexible working while contributing to exciting AI projects!

When answering this question, detail specific projects where you developed scalable AI applications. Highlight the technologies used, the challenges faced, and how your solutions impacted the project positively.

Discuss your experience working in teams and how you communicate with stakeholders to gather requirements and feedback. Highlight your ability to share ideas openly and emphasize the importance of collaboration in achieving project goals.

Summarize practices like continuous monitoring, performance metrics evaluation, and data validation processes. Emphasize methods for version control and testing that you use to maintain high standards for AI models.

Provide a brief overview of MLOps principles and discuss instances where you've implemented MLOps pipelines. Share insights on how this practice improved deployment efficiency and model lifecycle management.

Choose a specific project to discuss, detailing the problem to be solved, your role, and how you approached the challenge. Reflect on learnings and successes from the experience to show your growth and adaptability.

Mention sources such as online courses, professional forums, and industry conferences. Demonstrate your commitment to continuous learning and staying current in a rapidly evolving field.

Express your ability to facilitate open communication and discuss differences respectfully. Emphasize the goal of reaching a consensus through constructive dialogue and focusing on project objectives.

List relevant programming languages such as Python and frameworks or libraries like TensorFlow or PyTorch. Highlight your proficiency with tools like Azure AI services, which are critical for the role.

Discuss considerations such as user needs, data availability, and potential ethical concerns. Highlight the importance of designing for scalability and long-term usability in any AI solution.

Outline your approach, starting from assessment of the current ecosystem to identifying integration points. Discuss how you communicate with other teams and ensure the solution enhances overall functionality.