Machine Learning Engineer

Artificialy is an AI company based in Switzerland, with offices in Lugano and Zurich. We design and deliver AI solutions that are impactful, transparent, and tailored to real-world industry needs.

Founded in 2020 by pioneers with over 40 years of combined experience in the field, our team brings together talented engineers, physicists, and mathematicians from across Europe. We are driven by curiosity, technical excellence, and a shared ambition to build AI systems that make a tangible difference.

Machine Learning Engineer
, who will work with clients in the financial sector to bring AI solutions from prototype to production, with responsibilities spanning fine-tuning, deployment, inference optimization, monitoring, and continuous improvement. You will work in project teams alongside AI scientists, ensuring systems are reliable, scalable, and aligned with the high standards typical in banking.

This position can be based in Lugano or Zurich. You must be willing to work onsite at least 80% of the time. Candidates must be based in Switzerland or willing to relocate to Switzerland.

• Your responsibilities will span the full path from prototype to production - fine-tuning, deployment, inference optimization, monitoring, and continuous improvement of ML and LLM systems.
• Projects may involve LLM serving and inference stacks, cost/latency optimization, structured-output reliability, model monitoring pipelines, or product ionizing solutions designed alongside AI scientists.
• You will work in project teams alongside AI scientists, software engineering, infrastructure, and domain experts to integrate solutions smoothly into enterprise ecosystems using modern deployment tooling (Docker, Kubernetes, CI/CD).

• Master's degree in Computer Science, Engineering, or equivalent.
• 2+ years of working experience as ML Engineer or in a similar role
• Experience deploying and operating ML/LLM systems in production (serving, monitoring, cost/latency optimization).
• Strong programming skills (Python, SQL) and familiarity with deployment tooling (Docker, Kubernetes, CI/CD).
• Hands‑on experience with cloud platforms (AWS, Azure, or GCP).
• Proficiency in English.

• Experience with LLM inference stacks (e.g., vLLM) and structured-output reliability.
• Familiarity with Databricks or distributed data frameworks.
• Experience in finance or other regulated industries.

• Competitive compensation and growth opportunities
• A stimulating scientific environment with an informal working atmosphere