AI Engineer - Frontline Deployment & Automation

Tradition

Lausanne

On-site

CHF 120,000 - 180,000

Full time

6 hours ago
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Job summary

Tradition, the interdealer broking arm of Compagnie Financière Tradition, seeks a Forward Deployed AI Engineer based in Lausanne. You will collaborate with multiple business teams to understand workflows, design end-to-end AI solutions, and own delivery across discovery, build, deployment and iteration.

Expect hands-on development with Python, AI agents, and on-prem LLMs via vLLM, with CI/CD and observability practices.

Qualifications

  • Degree in Computer Science, Software Engineering, or a related field.
  • 2+ years of post-graduation experience as a software engineer with stakeholder exposure.
  • Strong Python and code maintainability; ability to hand off work clearly to others.
  • Strong stakeholder management: discovery conversations, scope of work, and prioritization.

Responsibilities

  • Understand workflows and scope end-to-end AI solutions across the business.
  • Map and redesign processes to automate activities and deliver measurable improvements.
  • Own solutions from discovery to deployment and ongoing refinement.
  • Build applications, AI agents and workflows using the platform and on-prem LLMs via vLLM.
  • Ensure deployment pipelines and CI/CD for AI applications; monitor health and performance.
  • Deliver demonstrations, training sessions, and clear documentation for adoption.

Skills

Python
Autonomy
Structured thinking
Stakeholder management

Education

CS/SE degree

Tools

Prometheus
Grafana
vLLM
Hermes Agent
OpenClaw
Langflow
n8n
LLM APIs

Job description

Tradition, the interdealer broking arm of Compagnie Financière Tradition, seeks a Forward Deployed AI Engineer based in Lausanne. You will collaborate with multiple business teams to understand workflows, design end-to-end AI solutions, and own delivery across discovery, build, deployment and iteration.

Expect hands-on development with Python, AI agents, and on-prem LLMs via vLLM, with CI/CD and observability practices.

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