AI Engineer

vor 2 Wochen

genève, Schweiz Adroit People Limited (UK) Vollzeit

Greetings We are Hiring AI Engineer- Geneva Switzerland AI Engineer- Using LLM/ Gen-AI Geneva Switzerland Min. 5 +Years. Permanent French – Optional and English Advanced. Hybrid Remote. 3 days a week in office. 2 days remote. Job Description: We are looking for an AI Engineer passionate about building practical AI solutions using Large Language Models (LLMs) and Generative AI. You will design and implement AI-driven applications that enhance business workflows, improve client experiences, and enable innovation across the organization. This is an exciting opportunity to work on cutting-edge AI technologies in a collaborative and dynamic environment. Key Responsibilities Develop and integrate LLM-powered applications (e.g., chatbots, summarization tools, workflow automation). Build and maintain RAG pipelines including data ingestion, embeddings, retrieval, and evaluation. Apply prompt engineering, model optimization, and implement guardrails for safe enterprise AI usage. Design scalable APIs and microservices for AI integration into enterprise platforms. Deploy models on AWS, Azure, or GCP using Docker and Kubernetes. Collaborate with product, data, and engineering teams to deliver impactful AI solutions. Stay updated with latest advancements in AI and ML frameworks (e.g., Hugging Face Transformers, LangChain, LlamaIndex, OpenAI, Anthropic, Meta Llama, Mistral). Required Skills Strong Programming Expertise: Proficiency in Python is essential, with experience in building APIs using frameworks like FastAPI, Flask, or similar. Ability to write clean, efficient, and maintainable code. Track record of hands-on AI and GenAI solution development and delivery, with a strong understanding of industry-standard frameworks and libraries. LLM Framework Experience: Hands-on knowledge of Large Language Model (LLM) ecosystems such as Hugging Face Transformers, LangChain, and LlamaIndex for developing advanced AI-driven applications. Vector Database Familiarity: Understanding and practical experience with vector search technologies like FAISS, Pinecone, Weaviate, or Milvus for implementing semantic search and retrieval. RAG Architecture & Embeddings: Solid grasp of Retrieval-Augmented Generation (RAG) workflows, embedding models, and techniques for optimizing retrieval performance in AI pipelines. Cloud & Containerization Skills: Experience deploying solutions on AWS, Azure, or GCP, along with containerization tools such as Docker and orchestration platforms like Kubernetes for scalable deployments. Problem-Solving & Communication: Strong analytical mindset to troubleshoot complex issues and excellent communication skills to collaborate effectively with cross-functional teams. Nice to Have Experience with CI/CD pipelines and cloud-native architectures. Exposure to financial services or regulated environments. Familiarity with agentic workflows, multi-step orchestration, and LangGraph.

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