Lead AI Systems Engineer - Multimodal & Real-Time

On; Zürich; Hybrid; CHF 140,000 - 200,000; Full time

Benefits offered by this job

Hybrid work arrangement

Relocation support

Job summary

On is seeking a Lead Software Engineer to anchor backend, data, and applied-AI architectures within the AI Lab and Emerging Technology Incubation team in Zurich. The role blends early-stage product exploration with engineering rigor to deploy scalable, production-grade systems and tools.

You will design resilient AI-native infrastructure, spanning streaming runtimes, memory structures, and retrieval pipelines, while ensuring cloud-native, secure, observable foundations that scale across

Qualifications

• Proven experience building production-grade multimodal agentic systems.
• Experience navigating enterprise cloud infrastructure (GCP) and aligning stakeholders.
• Strong foundations in Python (FastAPI, Pydantic, asyncio) and/or TypeScript (NestJS).

Responsibilities

• Lead backend, data, and applied-AI architecture across incubation ventures.
• Contribute across frontend and mobile surfaces to ship cohesive vertical slices.
• Design core infrastructure for real-time, low-latency systems and evaluation frameworks.
• Establish cloud-native, secure, observable platforms
• Collaborate with Product, Design, and Research to translate hypotheses into scalable software.

Skills

Multimodal systems

GCP/GKE

Python

TypeScript

PostgreSQL

Docker

Kubernetes

React

Flutter

API design

Education

Bachelor's degree in Computer Science or related field

Tools

Docker

Kubernetes

PostgreSQL

pgvector

FastAPI

Pydantic

asyncio

NestJS

React

Flutter

Job description

On is seeking a Lead Software Engineer to anchor backend, data, and applied-AI architectures within the AI Lab and Emerging Technology Incubation team in Zurich. The role blends early-stage product exploration with engineering rigor to deploy scalable, production-grade systems and tools.

You will design resilient AI-native infrastructure, spanning streaming runtimes, memory structures, and retrieval pipelines, while ensuring cloud-native, secure, observable foundations that scale across