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Open nowPosted 13 days ago

Applied AI Engineer Agentic Systems (m/f)

job-room.ch86,007 open roles

Where
Zürich, ZH, Switzerland
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Your applicationOpen nowApplied AI Engineer Agentic Systems (m/f)job-room.ch · Zürich, ZH, Switzerland

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  2. 3.4%3 days
  3. 7.9%7 days
  4. 14.2%14 days
  5. 34.1%30 days
This job: posted 13 days ago

The posting

Job Informationen ######

Location: Zurich (office, remote or on client site) Workload: Full-time Start: By agreement Your tasks: You design and build production-grade agentic systems end to end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management and lifecycle observability. You build and own RAG pipelines covering embeddings, chunking strategy, vector search and context window engineering against real quality targets. You integrate and abstract across multiple LLM providers with fallback routing and token, cost and latency management. You implement LLMOps in production: eval harnesses with real quality metrics, prompt versioning, observability tooling, cost and safety monitoring. You embed directly with client engineering teams for workshops, proofs of concept, code-with sessions and architecture walkthroughs. You build reusable patterns, accelerators and playbooks that scale beyond a single engagement. You define and use metrics for agent accuracy, latency, safety and cost-effectiveness and present findings in business terms. Your profile: Extensive software engineering experience in production environments Hands-on experience designing and deploying agentic AI solutions in production - non-negotiable Demonstrated experience with agentic orchestration frameworks such as LangGraph, CrewAI or AutoGen at production depth Direct experience calling LLM APIs in production code: provider abstraction, token management, latency and cost trade-offs RAG pipeline ownership: embeddings, chunking strategy, vector databases and context engineering LLMOps fundamentals: eval harness design, prompt versioning and production observability Cloud-native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD and infrastructure as code with Terraform or Helm Strong Python; Java or an equivalent backend language is acceptable Your benefits: Flexibility to work from an office, remotely or directly with clients Extensive learning and development opportunities Dedicated mentoring and structured onboarding from day one A workplace culture that celebrates diversity, inclusion and belonging

Benötigte Skills ######

  • Monitoring
  • Python
  • Machine Learning
  • JAVA
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