AI Solutions Architect

Introduction

About The Role And Potential Progression

That\’s in for you

Awareness instead of political decisions: Unleash your potential in a corporate culture that values personal responsibility and promotes data-driven decisions.
Dynamic learning: Develop yourself continuously with access to books, coaching and LinkedIn Learning – all without fear of making mistakes.
Excellent working environment: As a top company that has won multiple awards, we offer an environment that supports professional growth and guarantees appreciation.
Flexibility and equal opportunities: Use flexible working models with the option of working from home. With us, your contribution counts, regardless of your position.
Dog-friendly office: Bring your dog – all family members are welcome here.
Celebrate together: Experience how we celebrate successes together at team events, from summer parties to vineyard hikes.

How you can make an impact

Skills to be successful

1. AI and prompt expertise

1. In-depth knowledge of prompt engineering (optimisation of AI-supported interviews for knowledge generation)
2. Experience with LLMs (Large Language Models), especially in adapting and fine-tuning them for specific tasks

2. Knowledge Management & Information Preparation

1. Willingness and ability to learn knowledge management concepts and best practices
2. Understanding of knowledge structures, taxonomies and ontologies for efficient knowledge storage and reuse
3. Knowledge of natural language processing (NLP) for extracting and structuring expert knowledge
4. Experience with text classification, clustering and entity extraction for structured processing of responses

3. Technical skills & prototyping

1. Ability to independently implement ideas in prototypes and proof of concepts (PoCs)
2. Knowledge of Python or another relevant programming language for machine learning and AI prototypes
3. Experience with LLM APIs (e.g. OpenAI, Anthropic, Mistral) and frameworks such as LangChain or LlamaIndex
4. Basic knowledge of database technologies (e.g. PostgreSQL, vector databases)

4. Data processing & AI integration

1. Experience with data pipelines for knowledge processing and storage
2. Understanding of semantic search technologies for rapid retrieval of documented knowledge
3. Knowledge of knowledge graphs for intelligent linking of expert knowledge
4. Experience with RAG methods to improve knowledge retrieval accuracy

5. Innovative ability & problem-solving skills

1. Openness to new AI methods and technological developments
2. Ability to analyse complex problems and develop creative solutions
3. High level of self-motivation to test and further develop new concepts independently

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