AI / ML Engineer

At SEON, the AI / ML Engineer plays a crucial role in developing and enhancing machine learning applications. Key responsibilities include designing cutting-edge models, optimizing data science and MLOps infrastructure, transforming research prototypes into scalable systems, and improving the accuracy and efficiency of models. The engineer also collaborates with cross-functional teams to integrate AI capabilities into business solutions, ensuring seamless user experiences.

To apply for the AI / ML Engineer role at SEON, candidates should have a deep expertise in data science and strong software engineering skills, particularly in Python. Experience with machine learning and generative AI, as well as familiarity with MLOps tools and frameworks like Docker and Kubernetes, is essential. An understanding of modern LLM frameworks and real-time stream processing technologies will also give you a competitive edge.

SEON is deeply committed to the professional growth of its AI / ML Engineers. We offer access to significant opportunities for learning and development, including training on emerging AI trends and technologies. Additionally, the company provides resources such as private health insurance, language courses, and enhanced parental leave to support a well-rounded work-life balance and continuous learning.

As an AI / ML Engineer at SEON, you'll work with a variety of advanced technologies. This includes deep learning frameworks, model training and tuning tools, and MLOps practices. You’ll also engage with real-time data processing technologies like Kafka and Flink, as well as modern LLM frameworks like Hugging Face and LangChain to build AI/ML solutions that are efficient and scalable.

The culture for AI / ML Engineers at SEON is one of collaboration, creativity, and continuous innovation. Team members are encouraged to think outside the box and share their ideas in a fast-paced environment. Flexible working hours and a hybrid working setup foster a healthy work-life balance, while regular team activities, such as monthly breakfasts and language courses, help build a strong sense of community.

When answering this question, focus on specific projects or challenges you've encountered that demonstrate your machine learning expertise. Discuss the algorithms, tools, and frameworks you used, and explain how these solutions effectively addressed the problems at hand. Highlight your understanding of the impact and importance of ML in real-world scenarios.

In your response, mention specific practices you utilize, such as CI/CD pipelines, model versioning, and monitoring strategies. Provide examples of how you've implemented these in past projects to ensure continuous integration and smooth deployment of machine learning models, emphasizing the importance of reliability and efficiency.

Discuss your systematic approach to optimizing machine learning models by outlining techniques like hyperparameter tuning, feature selection, and algorithm choice. Emphasize the importance of balancing accuracy with efficiency and share examples of past experiences where you successfully improved model performance.

Share specific challenges you've encountered, such as scalability issues, data quality, or integration problems. Explain how you overcame these hurdles, detailing the lessons learned and the strategies you employed to ensure the successful transition from prototype to production.

Highlight key skills necessary for success in the AI / ML space, such as strong programming abilities, a deep understanding of ML algorithms, problem-solving skills, and an eagerness for continuous learning. Relate these skills back to your experiences and explain how they contribute to building effective AI solutions.

Discuss your strategies for incorporating model interpretability, such as using interpretable algorithms, implementing techniques like LIME or SHAP, and ensuring you communicate insights effectively to stakeholders. Use examples where possible, illustrating how explainability has added value to your projects.

Mention the specific tools and frameworks you have hands-on experience with, such as TensorFlow, PyTorch, or Scikit-learn for ML, and Docker or Kubernetes for MLOps. Discuss why you prefer these tools and how they have enhanced your workflow and project outcomes.

Explain your data management strategies, including data cleaning, preprocessing, and ensuring data quality. Discuss tools or methodologies you use to handle large datasets effectively, emphasizing the importance of maintaining data integrity and accessibility during the modeling process.

Share the methods you use to keep abreast of AI advancements, which could include following research journals, participating in online courses, attending conferences, or engaging with relevant online communities. Highlight specific resources or influential figures in the AI field that you follow for inspiration.

Provide an example of a project where you worked with team members from different disciplines. Share the steps you took to ensure effective communication, such as regular updates, clear documentation, or aligning on goals and expectations. Highlight how collaboration contributed to the success of the project and what you learned from the experience.