AI Engineer | Zurich | Switzerland

Job Location – Zurich, Switzerland Experience (Years): 6+ years Pay Rate – TBN Start Date

• ASAP 6 Months Contract – Renewable We are seeking an AI Engineer to collaborate with cross-functional teams in solving compliance and regulatory business challenges through innovative AI-powered solutions. 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.

Responsibilities

Collaborate with cross-functional teams to address compliance and regulatory business problems using AI-driven solutions. Contribute to the entire AI model lifecycle, including problem definition, development, training, fine-tuning, deployment, and ongoing improvements. Develop, test, and integrate AI-powered applications with strong adherence to software engineering best practices.

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 Tensor.

Flow, Py. Torch, 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. A collaborative team player with a proactive approach to problem-solving and adaptability to new technologies.

Degree in Computer Science, Software Engineering, Data Science, or a related field (or equivalent experience). Competencies Digital: Python Digital: Machine Learning Digital: Artificial Intelligence (AI)