We are the Swiss Leader in Online Banking and we provide trading, investing and banking services to +650’000 clients through our performant and secured digital platforms

We are all in at Swissquote. As an equal opportunity employer, we welcome candidates from all backgrounds, experiences and perspectives to join our team and contribute to our shared success

Our AI systems are already delivering measurable impact across the bank, from classical ML models powering core business processes to a growing suite of LLM-powered applications transforming how teams work. As we scale this portfolio, we need engineers who can push the boundaries of what's possible while keeping production systems robust and performant

Design and develop applications leveraging Large Language Models — including RAG systems, agentic workflows, and conversational interfaces — tailored to complex business needs in a regulated environment

Build LLM-Powered Applications:

• Train, fine-tune, and evaluate both classical ML and language models, selecting the right approach for each problem and optimizing for production constraints such as latency, cost, and accuracy
• Build reliable, scalable ML services and APIs. You care about code quality, testing, and maintainability as much as model performance
• Contribute to evaluation frameworks for AI applications — particularly for generative systems where traditional metrics fall short — and use them to drive continuous improvement
• Actively track the fast-moving LLM landscape, assess emerging tools and techniques (new model releases, orchestration frameworks, prompting strategies), and translate them into practical value for the team
• Work closely with data scientists and MLOps engineers, product owners, and business stakeholders to translate business problems into well-scoped AI solutions
• Collaborate Across Teams:

Qualifications

• Degree in Computer Science, Machine Learning, Data Science, Engineering, Mathematics, or a related field
• Strong programming skills in Python. You write clean, maintainable code and care about engineering best practices
• Hands-on experience building applications with Large Language Models (prompt engineering, RAG, fine-tuning, agent frameworks) — whether through professional experience, personal projects, or academic work
• Solid grounding in classical machine learning and deep learning. You can pick the right tool for the job, whether that's a gradient-boosted tree or a transformer
• You are able to build beyond the model — whether that means spinning up an API, putting together a web interface, or wiring up a data pipeline
• Familiarity with deploying models or applications into production. Experience with containerization (Docker, Kubernetes) and CI/CD is a strong plus

You understand the challenges of evaluating generative AI systems and can think critically about designing meaningful benchmarks beyond simple accuracy metrics

Fluent in English, able to collaborate effectively with both technical peers and non-technical stakeholders