AI Engineer | Zurich | Switzerland

Das ist der Job

Develop, test, and integrate AI‑powered applications with strong adherence to software engineering best practices.

Darum lohnt es sich

We are seeking an AI Engineer to collaborate with cross‑functional teams in solving compliance and regulatory business challenges through innovative AI‑powered solutions. Responsibilities Collaborate with cross‑functional teams to address compliance and regulatory business problems using AI‑driven solutions.

A collaborative team player with a proactive approach to problem‑solving and adaptability to new technologies. You will contribute across the full lifecycle of AI model development—from problem definition to deployment—ensuring high‑performance, scalable, and ethically responsible AI solutions.

In this role, you will work on model development, training, fine‑tuning, prompt engineering, and continuous improvement based on real‑world performance and stakeholder feedback. You will design, build, test, and integrate AI‑powered applications while adhering to best software engineering practices.

Effective communication with both technical and non‑technical stakeholders is essential to align project goals and deliverables. Contribute to the entire AI model lifecycle, including problem definition, development, training, fine‑tuning, deployment, and ongoing improvements. Implement and refine RAG‑based generative AI solutions and prompts.

Continuously evaluate and improve model performance using real‑world feedback. Communicate effectively with stakeholders across technical and business domains. Requirements 3–5 years of experience in software engineering or a related role, with exposure to AI/ML concepts and applications.

Strong proficiency in Python for production‑level software development. Hands‑on experience in application development , including building and deploying APIs. Familiarity with AI/ML frameworks such as TensorFlow, PyTorch, Hugging Face, and OpenAI API, and their integration into applications.

Experience with RAG‑based generative AI solutions and strong knowledge of prompt engineering . Understanding of machine learning concepts, including model types, training, fine‑tuning, and deployment. Knowledge of MLOps best practices (model lifecycle management, monitoring, scalability) is a plus.

Experience with source control, DevOps, and CI/CD pipelines (Git, Docker, Kubernetes). Strong understanding of software engineering principles , including scalability, performance optimization, and maintainability. Degree in Computer Science, Software Engineering, Data Science , or a related field (or equivalent experience). #J-18808-Ljbffr