Swissquote is a leading online banking and trading platform serving over 650,000 clients with secure, high‑performance digital services. Our team of more than 1,200 employees works in a flexible, multicultural environment, driving innovation in the financial industry.
Job Description
Our AI systems already deliver measurable impact across the bank, from classical ML models powering core processes to a growing suite of LLM‑powered applications. As we scale this portfolio, we need engineers who can push the boundaries of what’s possible while keeping production systems robust and performant. The Data Science team is looking for a Junior Machine Learning Engineer to design, build, and ship AI‑powered solutions with a strong emphasis on Large Language Model applications. You will be involved in projects end‑to‑end: from prototyping and experimentation through to production‑grade systems serving real users. This is a high‑impact role where you will take on real responsibility early and shape how AI is built and delivered across the organization.
As a Junior Machine Learning Engineer You Will
* Build LLM‑Powered Applications: 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.
* Develop & Fine‑Tune Models: 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.
* Engineer for Production: Build reliable, scalable ML services and APIs. You care about code quality, testing, and maintainability as much as model performance.
* Evaluate & Iterate: Contribute to evaluation frameworks for AI applications — particularly for generative systems where traditional metrics fall short — and use them to drive continuous improvement.
* Stay on the Cutting Edge: Actively track the fast‑moving LLM landscape, assess emerging tools and techniques, and translate them into practical value for the team.
* Collaborate Across Teams: Work closely with data scientists, MLOps engineers, product owners, and business stakeholders to translate business problems into well‑scoped AI solutions.
Qualifications
* Educational Background: Degree in Computer Science, Machine Learning, Data Science, Engineering, Mathematics, or a related field.
* Python Proficiency: Strong programming skills in Python. You write clean, maintainable code and care about engineering best practices.
* LLM & NLP Experience: Hands‑on experience building applications with Large Language Models (prompt engineering, RAG, fine‑tuning, agent frameworks) — through professional experience, personal projects, or academic work.
* ML Fundamentals: Solid grounding in classical machine learning and deep learning. You can pick the right tool for the job.
* Full‑Stack Comfort: You can build beyond the model — e.g., spinning up an API, creating a web interface, or wiring up a data pipeline.
* Production Mindset: Familiarity with deploying models or applications into production. Experience with containerization (Docker, Kubernetes) and CI/CD is a strong plus.
* Evaluation & Critical Thinking: You understand the challenges of evaluating generative AI systems and can think critically about designing meaningful benchmarks beyond simple accuracy metrics.
* Communication: Fluent in English, able to collaborate effectively with both technical peers and non‑technical stakeholders.
* Nice to Have: Experience with MLflow, fine‑tuning open‑source LLMs, or frontend frameworks (React, Streamlit, Gradio).
Swissquote is an equal opportunity employer and welcomes candidates from all backgrounds, experiences, and perspectives.
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