ML Platform Engineer | Zurich OR Fribourg | Switzerland

Acquism Sarl
Zürich, Switzerland
2 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Intermediate
Compensation
CHF 208K

Job location

Zürich, Switzerland

Tech stack

Artificial Intelligence
Cloud Computing
Information Engineering
Software Debugging
DevOps
Distributed Systems
Machine Learning
Software Tools
TensorFlow
Azure
Software Engineering
Systems Integration
PyTorch
System Availability
Delivery Pipeline
Spark
Kubernetes Helm Charts
Reliability of Systems
Backend
Event Driven Architecture
Data Lake
Kubernetes
Information Technology
ONNX (Open Neural Network Exchange) Format
Kafka
Machine Learning Operations
REST
Data Pipelines
Microservices

Job description

  • Design, develop, test, and maintain AI/ML infrastructure within a scalable microservices architecture running on Kubernetes.
  • Build and maintain high-quality, secure, and reliable DevOps pipelines and Helm charts
  • Work across the backend stack, integrating event-driven systems (Kafka), gRPC services, and REST APIs
  • Develop and optimize data pipelines using modern data engineering tools (e.g., Spark)
  • Manage ML lifecycle processes using tools such as MLflow
  • Contribute to architectural decisions to improve scalability, performance, and system reliability
  • Support deployment and monitoring of ML models in complex production environments, including isolated (air-gapped) setups with varying hardware constraints (CPU/GPU).
  • Ensure platform reliability and robustness in customer-deployed Kubernetes environments
  • Maintain high security and compliance standards aligned with industry best practices (e.g., ISO 27001

Requirements

  • 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, distributed systems, and data engineering technologies
  • Hands-on experience with ML platforms and frameworks (e.g., MLflow, PyTorch, SparkML)
  • Familiarity with modern data stack technologies (e.g., Spark, Delta Lake, TensorFlow, ONNX)
  • Experience building clean, maintainable, and testable systems following modern software engineering principles
  • Knowledge of cloud-native development and DevOps practices (including Helm)
  • Experience working in security-sensitive or highly regulated environments is a plus.
  • Strong problem-solving and debugging skills
  • Excellent communication skills in English and ability to collaborate across teams

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