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SeniorFull-timePythonAWSGCPData ScienceAgileAIML
Salary Range
$59,670 - $80,730 /year
This salary is estimated based on similar roles. The actual salary may vary.
As an AI Engineer  at Swiss Marketplace Group's Real Estate Business Unit, you will contribute to the data hub for the leading real estate platforms in Switzerland, which includes homegate.ch and immoscout24.ch among others. You will be part of a cross-functional, agile data team with an open mindset and teammates who enjoy learning new skills and sharing theirs.
You will design, build, and deploy AI solutions with a strong focus on LLM-powered systems, including chatbots, information retrieval, and generative AI applications. You will work as part of the Data Science & Engineering team, collaborating closely with data scientists, engineers, and product stakeholders to deliver production-ready AI features.
About The Position
• Design, build, and productionize LLM-based applications such as chatbots, RAG systems, summarization, classification, and evaluation pipelines.
• Own GenAI solutions end to end: from prototyping and architecture design to deployment, monitoring, scaling, and continuous improvement.
• Integrate LLMs with internal data sources using retrieval techniques, embeddings, vector search, and search infrastructure.
• Build reliable and maintainable ML / GenAI pipelines, including data ingestion, model orchestration, evaluation, deployment, observability, and alerting.
• Apply LLMOps and MLOps best practices for scalability, reliability, cost efficiency, explainability, and governance.
• Deploy and operate AI / ML systems in cloud environments such as AWS and GCP.
• Build production-grade APIs, services, and integrations that expose ML / LLM capabilities to internal and customer-facing products.
• Write, review, test, and debug high-quality Python code.
• Own and improve existing ML / AI systems, including recommendation, prediction, fraud, and classification systems.
• Collaborate with product, engineering, and business stakeholders to define objectives, success metrics, and production requirements.
• Report to the Head of Data Engineering & Science.
 
