Forward Deployed AI/ML Model Engineer - Senior Data Scientist

TMS Technology; Zürich; Vor Ort; CHF 140.000 - 190.000; Vollzeit

Zusammenfassung

TMS Technology in Zurich is hiring a Forward Deployed AI/ML Model Engineer to own end-to-end model development from training to deployment in client banking environments. The role combines hands-on engineering with architectural influence on the platform, including secure on-premises and air-gapped deployments.

You will work across fraud detection, AML and sanctions screening, with 5+ years of experience and a strong academic background from leading institutions.

Qualifikationen

• 5+ years hands-on ML model engineering experience.
• Strong academic background from ETH, EPFL, Oxford, MIT or equivalent.
• Production-grade experience with PyTorch, MLFlow, Spark and MLOps.
• Exposure to LLMs, agentic AI or GNNs is a strong advantage.
• Experience in security-sensitive or regulated deployment environments (finance, defence, health) is valued.

Aufgaben

• Build, fine-tune and own AI/ML models across fraud detection, AML and sanctions screening running on Kubernetes.
• Work across the full MLOps stack: training pipelines, inference workloads, model validation and data assets.
• Deploy models directly inside client banking environments, including air-gapped and on-premises scenarios.
• Apply rigorous evaluations and metrics to complex, real-world problems.
• Contribute to architecture decisions and feed client learnings back into the product roadmap.

Kenntnisse

ML model engineering

PyTorch

MLFlow

Spark

MLOps

LLMs

Security/regulatory deployment

Ausbildung

ETH/EPFL/Oxford/MIT or equivalent

Tools

Kubernetes

Jobbeschreibung

A global leader in the RegTech space with more than 1,500+ clients, 80 countries, and a household-name to banking clients, is hiring a Forward Deployed AI/ML Model Engineer to join their AI team in Zurich.

This is not a research role and it is not internal tooling. It is model to product end to end - taking ownership of ML models spanning fraud detection, AML and sanctions screening, developing and fine-tuning them from the inside, and deploying them directly into client banking environments including air-gapped and on-premises infrastructure.

The role exists because their models need someone who can walk into a bank, own the technical conversation, understand what breaks in a restricted environment before it breaks, and feed what they learn back into the product roadmap. Around 20 to 30% of the role involves direct client engagement. The rest is hands-on model engineering with genuine influence over where the platform goes next.

What you will be doing:

• Building, fine-tuning and owning AI/ML models across fraud detection, AML and sanctions screening running on Kubernetes
• Working across the full MLOps stack: training pipelines, inference workloads, model validation and data assets
• Deploying and tuning models directly inside client banking environments, including air-gapped and on-premises scenarios
• Applying rigorous evaluations and metrics to complex, real-world problems
• Contributing to architecture decisions and feeding client learnings back into the product roadmap

What they are looking for:

• 5+ years of hands-on ML model engineering experience
• Strong academic background - ETH, EPFL, Oxford, MIT or equivalent
• Production-grade experience with PyTorch, MLFlow, Spark, distributed training and MLOps
• Exposure to LLMs, agentic AI or GNNs is a strong advantage
• Experience in security-sensitive or regulated deployment environments (finance, defence, health) is highly valued
• AFC or financial crime domain exposure is a meaningful plus
• Right to work in Switzerland required, no sponsorship available

Full time permanent position in Zurich. 3 days on-site. Relocation to Switzerland considered