Generative AI Engineer

We're seeking a hands-on Generative AI Engineer with a background in Software, Data, or Machine Learning Engineering to join one of our clients in Geneva.

You'll design, build, and deploy generative-AI-driven solutions focused on real-world applications (i.e., no research-only roles). You'll work closely with engineering teams to implement practical AI capabilities using LLMs and RAG setups.

Key Requirements:

• Design, implement and maintain GenAI applications using frameworks such as RAG pipelines, agentic workflows, prompt engineering, MCP, and other emerging AI patterns.
• Partner with engineers and business users to incorporate GenAI applications into new or existing business workflows.
• Apply software engineering best practices - including code quality standards, testing, CI/CD, version control, and observability - to ensure scalable, secure, and maintainable AI solutions.
• Build production-grade APIs, microservices, and automation components that interface with GenAI systems and enterprise data platforms.
• Collaborate with architecture, data engineering, and security teams to ensure AI applications align with enterprise standards and integrate reliably with existing systems.

Do you have experience in Python?, Do you have a Master's degree?, * Degree in a field related to computer science or data science.

• Proven experience in building GenAI applications, including RAG pipelines, LLM integrations, and agent-based systems.
• Solid programming skills with Python, with experience writing maintainable, testable, and production-ready code.
• Strong understanding of LLM integration, vector databases, RAG, and MCP, with hands-on experience deploying AI models in production environments.
• Working knowledge of software engineering fundamentals, such as APIs (REST/GraphQL), microservices, object-oriented design, version control (Git), and automated testing.
• Experience with modern data platforms (e.g., Snowflake) and integrating structured/unstructured data sources into AI workflows.
• Experience with cloud platforms (e.g., AWS, Azure, GCP), including containerization (Docker), serverless patterns, and infrastructure-as-code.
• Familiarity with DevOps practices (CI/CD pipelines, monitoring, logging) is a plus.