Job Description
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.
Qualifications:
* 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.