Senior Data Scientist / AI Engineer (GenAI / RAG)

About

For an exciting project in the GenAI platform and Retrieval-Augmented Generation (RAG) area, we are looking for two Senior Data Scientists / AI Engineers to support a DevOps team. The focus is on consulting and implementing production-ready GenAI solutions and integrating modern AI technologies into existing enterprise systems.

Your Role

In this role, you will develop and implement robust, production-ready GenAI solutions, particularly in the area of Retrieval-Augmented Generation. You will work with modern AI technologies and established software engineering practices.

Responsibilities

• Development and implementation of scalable GenAI and RAG solutions
• Building, deploying, and maintaining data pipelines and APIs
• Integration of GenAI applications, inference services, and real-time interfaces into enterprise systems
• Collaboration with Cloud Engineers, Data Engineers, Data Scientists, DevOps teams, and business units
• Transitioning prototypes into stable, production-ready solutions
• Monitoring, fine-tuning, and optimizing machine learning and GenAI solutions in production environments
• Ensuring reliability, scalability, and business alignment of AI solutions

Qualifications

• Retrieval-Augmented Generation, Prompt Engineering, and Context Engineering
• LangChain, LangFuse, NeMo Guardrails, SpringAI, or comparable frameworks
• Azure Cloud Platform Engineering
• Azure App Service, Azure Container Apps, Azure AI Foundry, and Azure AI Search
• API development for model integration
• CI/CD pipelines and automated deployments
• LLM tracing, observability, and OpenTelemetry
• Building secure and scalable cloud solutions
• Azure network services such as Firewall, vNET, and Load Balancer
• TLS, SSL certificates, and development in regulated environments
• Python and Java
• Angular, NodeJS, Spring
• Bash, PowerShell, or comparable scripting languages
• Test automation and iterative validation of GenAI solutions
• Statistics, feature engineering, and model evaluation