PpBuilding the bank of tomorrow takes more than skills.br/It means combining our differences to imagine, discuss, code, develop, test, learn… and celebrate every step together. Share our vibes? Join Swissquote to unleash your potential. /p pWe 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. /p pOur +1200 employees work in a flexible way, without dress code and in multicultural teams.br/By having a huge impact on the industry, they are growing their skills portfolio and boosting their career in a fast-pace environment. Have a look behind the scenes by checking Humans of Swissquote on instagram. /p pWe 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. /p pAre you all in? Don’t be shy, apply! /p h3Job Description /h3 pOur 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. /p pThe Data Science team is looking for a bJunior Machine Learning Engineer /b 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. /p pAs a Junior Machine Learning Engineer You Will: /p ul libBuild LLM-Powered Applications: /b 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. /li libDevelop Fine-Tune Models: /b 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. /li libEngineer for Production: /b Build reliable, scalable ML services and APIs. You care about code quality, testing, and maintainability as much as model performance. /li libEvaluate Iterate: /b Contribute to evaluation frameworks for AI applications — particularly for generative systems where traditional metrics fall short — and use them to drive continuous improvement. /li libStay on the Cutting Edge: /b 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. /li libCollaborate Across Teams: /b Work closely with data scientists and MLOps engineers, product owners, and business stakeholders to translate business problems into well-scoped AI solutions. /li /ul h3Qualifications /h3 ul libEducational Background: /b Degree in Computer Science, Machine Learning, Data Science, Engineering, Mathematics, or a related field. /li libPython Proficiency: /b Strong programming skills in Python. You write clean, maintainable code and care about engineering best practices. /li libLLM NLP Experience: /b 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. /li libML Fundamentals: /b Solid grounding in classical machine learning and deep learning. You can pick the right tool for the job, whether that means a gradient‑boosted tree or a transformer. /li libFull‑Stack Comfort: /b 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. /li libProduction Mindset: /b Familiarity with deploying models or applications into production. Experience with containerization (Docker, Kubernetes) and CI/CD is a strong plus. /li libEvaluation Critical Thinking: /b You understand the challenges of evaluating generative AI systems and can think critically about designing meaningful benchmarks beyond simple accuracy metrics. /li libCommunication: /b Fluent in English, able to collaborate effectively with both technical peers and non‑technical stakeholders. /li libNice to Have: /b Experience with MLflow, fine‑tuning open‑source LLMs, or front‑end frameworks (React, Streamlit, Gradio). /li /ul /p #J-18808-Ljbffr