Staff Backend Engineer, AI Platform
SwitzerlandFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 8+ years of software engineering experience, including at least 2 years in a Staff or equivalent senior technical role.
- Required Skills
- AWSPHPPythonKubernetesMLFlowContent managementGoLangChain
Requirements
- 8+ years of software engineering experience, including at least 2 years in a Staff or equivalent senior technical role.
- Strong Python expertise with experience building and maintaining production-grade backend services.
- Proven experience designing, shipping, and operating LLM-based or AI-powered systems in production environments.
- Strong system design skills with the ability to build scalable, maintainable, and extensible architectures.
- Familiarity with modern AI tooling ecosystems such as LangChain, LangGraph, LiteLLM, Langfuse, Phoenix, MLflow, or similar.
- Experience or strong exposure to multi-agent systems, including orchestration, tool calling, and context management (strong plus).
- Comfort working across heterogeneous environments including Python, Go, PHP, and frontend systems.
- Strong infrastructure knowledge with AWS, Kubernetes, and CI/CD pipelines.
- Excellent communication and influence skills, with the ability to drive adoption across multiple engineering teams.
Responsibilities
- Own the architecture and technical vision of the AI Platform, including multi-agent orchestration, LLM gateway, evaluation systems, and observability infrastructure.
- Design and build production-grade AI services that enable scalable, reliable, and safe deployment of AI features across the organization.
- Establish evaluation frameworks, quality gates, and testing systems for prompts and AI workflows, ensuring measurable confidence in production AI systems.
- Define and evolve LLM gateway capabilities including routing, failover, cost tracking, and rate limiting.
- Drive cross-team adoption by creating integration standards, internal documentation, and reusable blueprints for AI development.
- Influence engineering-wide practices around prompt engineering, agent design, testing, and observability.
- Mentor engineers and elevate technical standards through code reviews, design discussions, and hands-on collaboration.
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