Location: Zurich OR Fribourg (Switzerland)
Work Model: Hybrid – 3 days in office
Contract Type: Permanent
Responsibilities
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Design, develop, test, and maintain AI/ML infrastructure within a scalable microservices architecture running on Kubernetes.
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Build and maintain high-quality, secure, and reliable DevOps pipelines and Helm charts
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Work across the backend stack, integrating event-driven systems (Kafka), gRPC services, and REST APIs
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Develop and optimize data pipelines using modern data engineering tools (e.g., Spark)
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Manage ML lifecycle processes using tools such as MLflow
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Contribute to architectural decisions to improve scalability, performance, and system reliability
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Support deployment and monitoring of ML models in complex production environments, including isolated (air-gapped) setups with varying hardware constraints (CPU/GPU).
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Ensure platform reliability and robustness in customer-deployed Kubernetes environments
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Maintain high security and compliance standards aligned with industry best practices (e.g., ISO 27001
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Degree in Computer Science, Engineering, or equivalent practical experience
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5+ years of experience in AI/ML platform engineering or related roles
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Strong experience with Kubernetes, distributed systems, and data engineering technologies
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Hands-on experience with ML platforms and frameworks (e.g., MLflow, PyTorch, SparkML)
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Familiarity with modern data stack technologies (e.g., Spark, Delta Lake, TensorFlow, ONNX)
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Experience building clean, maintainable, and testable systems following modern software engineering principles
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Knowledge of cloud-native development and DevOps practices (including Helm)
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Experience working in security-sensitive or highly regulated environments is a plus.
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Strong problem-solving and debugging skills
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Excellent communication skills in English and ability to collaborate across teams
