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AI Solutions Engineer; BE-CSS-ISA--LD

Job in Genf, Geneva, Switzerland
Listing for: CERN European Organization for Nuclear Research
Full Time position
Listed on 2026-06-09
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 80000 - 100000 CHF Yearly CHF 80000.00 100000.00 YEAR
Job Description & How to Apply Below
Position: AI Solutions Engineer (BE-CSS-ISA-2026-115-LD)
Location: Genf

AI Solutions Engineer (BE-CSS-ISA--LD)

  • Contract

At CERN, the European Organization for Nuclear Research, physicists and engineers are probing the fundamental structure of the universe. Using the world's largest and most complex scientific instruments, they study the basic constituents of matter – fundamental particles that are made to collide together at close to the speed of light. The process gives physicists clues about how particles interact, and provides insights into the fundamental laws of nature.

Introduction

You will work at the forefront of applying artificial intelligence to accelerator control systems and operational workflows king collaboratively across the BE‑CSS group and the broader Accelerator and Technology Sector (ATS), you will design, develop, and deploy practical AI solutions — from LLM‑based knowledge assistants to agentic AI workflows.

A defining aspect of your role will be contributing to the newly established ATS AI Core Team, where you will provide expertise in LLM and agentic AI systems, secure knowledge connectors, and safe agent deployment patterns.

Your work will span hands‑on software development, cross‑group collaboration, and technical leadership — an excellent opportunity to work alongside engineers and scientists from across CERN and to shape the organisation’s AI capabilities in a meaningful and lasting way.

Functions
  • Contribute to the AI‑driven evolution of the BE‑CSS group’s software product portfolio, identifying and implementing opportunities to embed AI capabilities across group applications.
  • Design and develop LLM‑based solutions, including a first‑line support chatbot backed by retrieval‑augmented generation (RAG) pipelines, vector databases, and secure knowledge connectors, in close collaboration with the CERN IT department providing the underlying MLOPs substrate.
  • Join the ATS AI Core Team and take a major role in the rollout of LLM/agentic AI and knowledge systems, contributing to reusable patterns, evaluation frameworks, and safe agent deployment practices across the sector.
  • Collaborate with engineers, domain experts, and scientists from across CERN —including CERN IT, BE‑CSS colleagues, and other ATS groups — to align AI solutions with operational needs and organisational standards.
  • Contribute to other AI‑related initiatives in the BE‑CSS group and across the ATS sector, sharing expertise and adapting to emerging priorities in the rapidly evolving field of applied AI.
  • Help define and deliver the central AI infrastructure needed across ATS, contributing to shared tooling, reference architectures, MLOps practices, and evaluation frameworks that enable groups to adopt AI solutions safely and efficiently.
  • Master’s Degree or equivalent relevant experience in the field of Computer Science or a related field.
Experience
  • Solid experience in Python software development, including the design and delivery of production‑ready applications in a collaborative engineering environment.
  • Practical knowledge of LLM frameworks (such as Lang Chain or Llama Index) and hands‑on experience building retrieval‑augmented generation (RAG) pipelines and working with vector databases, chunking and embedding techniques.
  • Familiarity with agentic AI orchestration approaches and the Model Context Protocol (MCP), or a strong motivation and demonstrated ability to develop this expertise rapidly.
  • Understanding of evaluation and safety frameworks for LLM‑based systems, including techniques for assessing reliability, output quality, and responsible deployment.
  • Experience with MLOps practices and CI/CD workflows; ability to write clear technical documentation and communicate complex ideas to diverse audiences.
  • Ability to build effective working relationships and communicate clearly with colleagues from diverse scientific and engineering backgrounds, including non‑specialist audiences.
  • Curiosity about the application of AI in complex scientific and operational environments, with an openness to learning the accelerator domain context through direct engagement with its challenges.
  • Knowledge of programming techniques and languages: notably Python.
  • Architecture and design of ICT systems.
  • Identification and…
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