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 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.

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)

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