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2 machine learning engineer jobs found in Geneva

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Data Science Geneva machine learning engineer
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DevOps  (3) Machine Learning  (2) Business Intelligence  (2) Software Engineering  (2) Data Engineering  (1) Data Analytics  (1)
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Full time  (2)
Ekkiden
15/04/2026
Full time
IP Telephony & Linux Engineer - DevOps (M/F/X)
Ekkiden Geneva, Geneve, Switzerland
Ekkiden, an international consulting group founded in 2019, fosters an ecosystem of passionate and committed consultants who lead organizational, operational and technological transformation projects in IT/Digital, Industry/R&D and sustainability, for large accounts and SMEs, in France, Switzerland, Spain and Germany. The role: We are looking for a   Telephony   , Linux and DevOps Engineer to support the teams on: The operation and industrialization of the enterprise telephony platform Standardization and automation of application deployments Improving the reliability of production deployments through DevOps tools (GoCD and equivalents) The mission is long-term and aims to sustainably structure operational and telephony practices. Responsibilities: Telephony Administration, maintenance and development of IP telephony and unified communications solutions (IPBX, VoIP, SIP, operator trunks) Expertise in   Cisco Call...
DSM-Firmenich
17/04/2026
Full time
Senior Scientist - Data Science & AI, Industrial Process Analytics
DSM-Firmenich Geneva, Geneve, Switzerland
Senior Scientist — Data Science & AI, Industrial Process Analytics Location: Delft(NL) OR Geneva(CH) Are you excited to apply modern data science to real production plants, help process engineers act faster, smarter, and deliver measurable sustainability impact? Join our Industrial Process Analytics team to build advanced analytics that optimize chemical and biochemical manufacturing at scale. Key responsibilities: Design, build, and ship scalable data science applications for manufacturing - from first idea to validated, deployed product. Implement model‑ and data‑driven approaches that support plant engineers and operators in day‑to‑day decisions. Collaborate in multi‑disciplinary teams (data scientists, process/chemical engineers, cloud & data engineers, reliability and production experts) to improve yield, throughput, quality, reliability, and sustainability. Generate insights and convert them into measurable impact. Champion a data‑driven mindset...

 

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