AI Engineer

Job Description:

AI Engineer/AI Developer

Geneva, Switzerland-Onsite

Permanent

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.