Senior GenAI Data Scientist (m/f/d)

The AutoScout24 Group is seeking a seasoned Senior GenAI Data Scientist to join their dynamic team. This remote position offers a unique opportunity to be at the forefront of innovation in the digital automotive marketplace, driving the development and implementation of cutting-edge Generative AI solutions. If you're passionate about shaping the future of AI and have a knack for translating complex product requirements into scalable, production-ready systems, this role is designed for you. AutoScout24 is an organisation that thrives on ambition and a collective drive to excel, fostering an international community where individuals can put their ideas into action and truly make an impact.

About the Role

This is a hands-on, expert-level position focused on building and deploying sophisticated Generative AI systems. You will be instrumental in designing agentic AI architectures, leveraging modern frameworks like LangChain, AutoGen, or Atomic Agents. A key aspect of this role involves championing "harness engineering" practices, establishing robust workflows, standards, and feedback loops that empower software engineering agents to reliably generate, test, maintain, and review code across AI, ML, and data products. Your contributions will be crucial in integrating these advanced AI components into AutoScout24's platforms, workflows, and customer-facing products, ensuring seamless integration through well-defined APIs and scalable deployment patterns.

The role also demands a deep understanding of knowledge integration, particularly through Retrieval-Augmented Generation (RAG) architectures, custom model fine-tuning, and vector search techniques. You'll be responsible for ensuring that AI systems possess the necessary context for accurate and relevant outputs. Furthermore, you will define and implement evaluation strategies, develop ground-truth datasets, and establish automated testing and monitoring frameworks to guarantee stable, long-term model performance and proactively identify issues such as data drift or model hallucinations. As a senior member of the team, you will also play a vital role in mentoring peers, guiding solution architecture, and promoting best practices in coding, testing, documentation, and observability across the broader team.

Your responsibilities will span the entire lifecycle of AI product development, with a strong emphasis on innovation and practical application:

• **GenAI System Design and Development:** Architect, build, and deploy agentic GenAI systems using contemporary frameworks, translating product needs into scalable AI solutions.
• **Harness Engineering:** Establish and maintain agent-ready engineering environments, including repositories, workflows, standards, and feedback loops, to facilitate automated code generation and maintenance by AI agents.
• **Knowledge Integration:** Develop and manage pipelines for knowledge integration, including RAG, model fine-tuning, and vector search, to enhance AI system context and accuracy.
• **Evaluation and Monitoring:** Define evaluation metrics, create ground-truth datasets, and implement automated testing and monitoring for long-term model performance and issue detection.
• **Cross-Functional Collaboration:** Partner with engineering, data science, and product teams to integrate AI components into existing platforms and customer-facing applications, ensuring robust API design and deployment.
• **Technical Leadership and Mentoring:** Act as a subject matter expert in GenAI and agentic software engineering, providing guidance, mentorship, and promoting best practices.
• **Research and Prototyping:** Experiment with new AI tools, models, and architectures, identifying high-impact opportunities and developing prototypes for potential production deployment.

What They're Looking For

To thrive in this role, you should possess a strong foundation in computer science, data science, or a related field, backed by practical experience. Key requirements include:

• **AI/ML Expertise:** Proven experience in designing and implementing agentic AI systems, including practical knowledge of RAG pipelines, vector databases, embeddings, and fine-tuning workflows.
• **Software Engineering Acumen:** Strong coding skills, particularly in Python, and a solid understanding of robust software engineering practices. Experience in designing maintainable, agent-ready engineering environments with well-defined repository structures, coding standards, automated testing, and CI/CD workflows is essential.
• **Cloud and MLOps Experience:** Familiarity with deploying AI systems in cloud environments and experience working with modern ML stacks, including MLOps principles.
• **Communication Skills:** The ability to articulate complex AI concepts clearly to both technical and non-technical stakeholders.
• **Problem-Solving and Initiative:** A high level of motivation, a strong sense of ownership, and a demonstrable drive to build impactful AI products.
• **Language Proficiency:** Fluency in English is required, with German being a beneficial asset.
• **Bonus Qualifications:** Experience with knowledge graphs, graph-based retrieval, semantic modelling, or ontology-driven knowledge representation would be advantageous. Familiarity with prompt engineering or LLM-based application design is also a plus.

In South Africa, senior data scientist roles, especially those focusing on advanced AI and GenAI, can command competitive salaries often ranging from R60,000 to R100,000+ per month, depending on experience and specific industry. Employers often include large financial institutions, tech companies, and established e-commerce platforms. Career progression typically involves moving into lead data science positions, management roles, or specialisation in niche AI fields.

• A degree in computer science, data science, engineering, or a related field.
• Experience designing maintainable, agent-ready engineering environments, including repository structures, coding standards, automated test suites, CI/CD workflows, documentation patterns, and feedback mechanisms that allow software engineering agents to safely generate, modify, review, and maintain code.
• Experience with knowledge graphs, graph-based retrieval, semantic modelling, or ontology-driven knowledge representation is a plus.
• Strong coding skills in Python and experience with robust software engineering practices.
• Practical experience with RAG pipelines, vector databases, embeddings, or fine-tuning workflows.
• Strong understanding of common challenges in AI development (ground-truth evaluation data, monitoring, integration complexity, model drift).
• Experience deploying AI systems in a cloud environment and working with modern ML stacks.
• Familiarity with MLOps, prompt engineering, or LLM-based application design.
• Ability to explain complex AI topics in clear and actionable language to technical and non-technical stakeholders.
• High motivation, ownership mentality, and a strong drive to build impactful AI products.
• Fluency in English; German is a plus.