Machine Learning Engineer

Artificialy; Lugano; Hybrid; CHF 85’000 - 120’000; Vollzeit

Zusammenfassung

A technology-driven company in Switzerland is seeking a Machine Learning Engineer. This role involves designing, developing, and implementing data-driven solutions for clients in the financial sector. The ideal candidate will have a Master's degree or PhD, strong programming and ML skills, and experience with cloud platforms. The position offers a full-time permanent contract in an informal yet stimulating environment that promotes ongoing training and collaboration with research teams.

Leistungen

Competitive compensation

Growth opportunities

Informal working atmosphere

Ongoing training and mentoring

Qualifikationen

• EU or Swiss nationality / C permit required.
• 2+ years of experience as a ML Engineer or a PhD.
• Fluent English (min. C1) is required.

Aufgaben

• Design, develop, and operationalize data-driven ML solutions.
• Collaborate with teams across software engineering, infrastructure, and business domains.
• Ensure reliability and scalability of ML models in cloud environments.

Kenntnisse

Python

SQL

TensorFlow

PyTorch

Scikit-learn

Data visualization (e.g., Tableau, Matplotlib)

Git-based development workflows

Natural Language Processing

Ausbildung

Master’s degree or PhD in Computer Science, Mathematics, Physics, Informatics, Engineering

Tools

AWS

Azure

GCP

Docker

Kubernetes

Jobbeschreibung

Machine Learning Engineer

Lugano/Zürich Switzerland

Hard requirements (please DO NOT APPLY if you don't match all):

• You must have EU or Swiss nationality / C permit.
• You must have 2+ years of experience as a ML Engineer in a similar role or a PhD.
• You must be willing to work at least 80% onsite in our office in Lugano / Zürich and potentially on client’s site.
• You must speak fluently English (min. C1).

Role

As a Machine Learning Engineer, you will work with clients in the financial sector to design, develop, and operationalize data-driven solutions. Your responsibilities will span the full lifecycle of ML initiatives spanning from early experimentation to deployment, monitoring, and continuous optimization.

Projects may involve automated or agentic analytical pipelines, forecasting models, anomaly-detection systems, or other statistical and machine-learning solutions. Much of the work will run within cloud-based environments, where you will ensure that pipelines and models are reliable, scalable, and aligned with the high standards typical in a regulated environment.

You will collaborate with software engineering, infrastructure, and domain teams to integrate solutions smoothly into enterprise ecosystems. Strong interpersonal skills will support effective communication with stakeholders both onsite and remotely.

Required Skills

• Master’s degree or PhD in Computer Science, Mathematics, Physics, Informatics, Engineering, or equivalent discipline.
• Strong programming skills (Python, SQL, …) and familiarity with ML libraries (TensorFlow, PyTorch, Scikit-learn, etc.).
• Solid understanding of ML principles, statistical modeling, and modern data-processing techniques, especially in the field of Natural Language Processing.
• Hands-on experience with cloud platforms (AWS, Azure, or GCP) and with deploying ML systems in scalable, production-grade environments.
• Experience with data visualization tools (e.g., Tableau, Matplotlib).
• Familiarity with Git-based development workflows (GitHub/GitLab/Bitbucket).
• Proficiency in English.

Desirable Skills

• Familiarity with cloud ML services and hybrid cloud architectures (e.g., AWS SageMaker, Azure ML, Vertex AI).
• Knowledge of deployment and orchestration tools (Docker, Kubernetes, CI/CD).
• Understanding of data systems and architectures (e.g., PostgreSQL, distributed data frameworks).
• Proficiency in Italian or German; French is a plus.

We offer

• Full-time permanent contract.
• Competitive compensation and growth opportunities.
• A stimulating scientific environment with an informal working atmosphere.
• Ongoing training, mentoring, and close collaboration with cutting-edge research teams.