Junior Machine Learning Engineer

Nyon - Switzerland

Monthly Salary: Not Disclosed

Posted on: 17 hours ago

Vacancies: 1 Vacancy

Job Summary

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 whats 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 (new model releases orchestration frameworks prompting strategies) and translate them into practical value for the team.
Collaborate Across Teams: Work closely with data scientists and 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) whether 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 whether thats a gradient-boosted tree or a transformer.
Full-Stack Comfort: 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.
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).

Remote Work :

Employment Type :

Full-time

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 (new model releases orchestration frameworks prompting strategies) and translate them into practical value for the team.
Collaborate Across Teams: Work closely with data scientists and 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) whether 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 whether thats a gradient-boosted tree or a transformer.
Full-Stack Comfort: 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.
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).

Remote Work :

Employment Type :

Full-time

Key Skills

Industrial Maintenance
Machining
Mechanical Knowledge
CNC
Precision Measuring Instruments
Schematics
Maintenance
Hydraulics
Plastics Injection Molding
Programmable Logic Controllers
Manufacturing
Troubleshooting

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About Company

Swissquote

As a leading provider of online financial services, Swissquote Group offers innovative solutions and comprehensive services to meet the wide-ranging demands of its global clients.With an Online Trading Platform linked to more than 60 stock markets in over 40 countries, including off-e ... View more

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