Senior ML Platform Engineer | Kubernetes & MLOps

Acquism SARL; Zürich; Hybrid; CHF 140,000 - 210,000; Full time

Job summary

Acquism SARL in Zürich is seeking an experienced AI/ML platform engineer to design, build, test, and operate scalable ML infrastructure within Kubernetes-based microservices. The role emphasizes DevOps, data pipelines, and secure, compliant production environments.

You will collaborate across teams, own ML lifecycle tooling (MLflow, PyTorch, Spark), and support air-gapped deployments with varying hardware constraints while maintaining ISO 27001-aligned security standards.

Qualifications

• Degree in Computer Science, Engineering, or equivalent practical experience.
• 5+ years of experience in AI/ML platform engineering or related roles.
• Strong experience with Kubernetes and distributed systems.
• Excellent communication in English and collaboration across teams.

Responsibilities

• Design, develop, test, and maintain AI/ML infrastructure within Kubernetes.
• Build and maintain secure DevOps pipelines and Helm charts.
• Integrate event-driven systems (Kafka), gRPC, and REST APIs.
• Develop data pipelines using Spark and related tools.
• Manage ML lifecycle with MLflow.
• Enhance scalability, performance, and reliability of platforms.
• Support deployment and monitoring in air-gapped production environments.
• Ensure ISO 27001-aligned security and compliance.

Skills

AI/ML platform engineering

Kubernetes

Distributed systems

Data engineering

Problem solving

English communication

Education

Degree in Computer Science, Engineering, or equivalent practical experience

Tools

Kubernetes

MLflow

PyTorch

SparkML

Spark

Delta Lake

TensorFlow

ONNX

gRPC

REST APIs

Kafka

Helm

Job description

Acquism SARL in Zürich is seeking an experienced AI/ML platform engineer to design, build, test, and operate scalable ML infrastructure within Kubernetes-based microservices. The role emphasizes DevOps, data pipelines, and secure, compliant production environments.

You will collaborate across teams, own ML lifecycle tooling (MLflow, PyTorch, Spark), and support air-gapped deployments with varying hardware constraints while maintaining ISO 27001-aligned security standards.