Perus

Lead AI Engineer (Agentic AI)

Zürich, Zurich, Switzerland Yhtiö: TN Switzerland Asiakas / Työnantaja: Visium
Julkaistu: 18.05.2026
Sulkemispäivä: 02.07.2026
Työpaikkaviite: 2eb280122da97915ab997f3a45d38612

Työpaikkatiedot

Sijainti
Zürich, Zurich, Switzerland
Yhtiö
TN Switzerland
Asiakas / Työnantaja
Visium
Työpaikkaviite
2eb280122da97915ab997f3a45d38612
Listaustyyppi
Perus
EU-työlupa vaaditaan
Ei
Julkaistu
18.05.2026
Sulkemispäivä
02.07.2026

Työkuvaus

Role

You will own and evolve the agentic layer that powers our products. That means designing and building the multi-agent orchestration systems, RAG pipelines, tool-use architectures, and workflow engines that sit at the core of what we build.

This is a senior engineering role with real production stakes. You will make foundational architecture decisions, shape engineering standards, and work directly with product and leadership from day one. It is not a research role. It is a building role.

Key Responsibilities:

  • Design and implement multi-agent architectures using Agno, LangChain, and LangGraph
  • Build and maintain scalable backend services and APIs in Python and FastAPI
  • Own agent orchestration logic: task routing, tool use, state management, and human-in-the-loop integration
  • Design and implement RAG pipelines over domain-specific data, with strict requirements for accuracy, traceability, and compliance
  • Make production-grade decisions on LLM integration, prompting strategies, and reliability patterns
  • Set engineering standards for code quality, testing, and deployment of agentic systems
  • Drive end-to-end feature ownership from design through to production
  • Mentor engineers and actively shape the engineering culture as the team grows
  • Collaborate closely with product and leadership to influence the technical roadmap
  • Requirements

    You bring technical strength, curiosity, and a drive to build meaningful AI systems. Here is what helps you succeed in this role:

  • A strong Python engineer who has shipped production systems, not just prototypes
  • Hands-on with agentic frameworks (LangChain, LangGraph, Agno, or similar)
  • Fluent in LLMs in production: prompting, tool use, orchestration, failure modes
  • Experience building production RAG systems: retrieval architectures, vector databases, embedding strategies, and evaluation frameworks
  • Experience with agent evaluation frameworks and observability tooling ( LangSmith or similar): tracing agent behavior, debugging multi-agent interactions, and ensuring reliability in production
  • Comfortable with cloud environments (AWS, GCP, or Azure) and distributed system design
  • Experienced with MLOps practices: CI/CD, monitoring, deployment pipelines
  • Autonomous, low ego, and serious about what you build
  • Excited about working in a domain where the quality of your work has real-world consequences
  • Nice to have:

  • Experience in life sciences, regulated industries, or GxP-compliant environments
  • Experience with multi-agent systems and workflow orchestration
  • We know that great candidates might not check every box. If you’re excited about this role and believe you could contribute, we’d love to hear from you.

    Benefits

    What we offer

  • A competitive compensation package
  • A flexible working culture because your work-life balance matters to us
  • A position that enables you to have an impact on 1’000s of people, and the whole company's growth.
  • An international, knowledgeable, and passionate team with a strong collaborative mindset
  • Employee Stock Ownership Plan
  • Why Join Us

  • Work with autonomous agents, orchestration runtimes, and knowledge retrieval systems
  • Build technology that transforms how life-science experts research, decide, and create
  • Shape architecture, culture, and direction from the very beginning
  • Collaborate with world-class engineers and researchers who share a deep passion for AI
  • Hybrid setup with high trust, autonomy, and flexibility
  • Competitive compensation and the chance to be part of a company defining a new AI category
  • Taidot

    Agile Project Management Algorithms analyse big data analyse business requirements apply ICT systems theory apply systemic design thinking Artificial Neural Networks Assembly (computer programming) assess ICT knowledge build business relationships build predictive models build recommender systems Business Analytics Business Intelligence business process modelling C COBOL CoffeeScript Common Lisp computer programming Computer Simulation Computer Vision create data sets creatively use digital technologies Data Mining Data Models Data Science database development tools Deep Learning define technical requirements deliver visual presentation of data design application interfaces design database scheme design process develop creative ideas develop statistical software digital data processing Erlang Groovy Haskell ICT project management methodologies identify processes for re-engineering Information Architecture information categorisation Information Extraction information structure Java (computer programming) JavaScript lean project management LINQ Lisp manage business knowledge manage ICT data classification manage ICT semantic integration Matlab Microsoft Visual C++ ML (computer programming) N1QL Objective-C OpenEdge Advanced Business Language operational research Pascal (computer programming) perform dimensionality reduction Perl PHP principles of artificial intelligence Process-based management Prolog (computer programming) Python (computer programming) R resource description framework query language Ruby (computer programming) SAP R3 SAS language Scala Scratch (computer programming) Smalltalk (computer programming) SPARQL Swift (computer programming) systems development life-cycle task algorithmisation TypeScript Unstructured Data use data processing techniques utilise machine learning VBScript Visual Basic visual presentation techniques

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