What you will do as an AI Engineer
You play a central role in scaling our Composable Banking Platform, ensuring seamless integration across business lines. By advocating responsible AI and developing transparent, value-driven applications, you help deliver software that improves efficiency, enhances decision-making, and creates measurable impact for our clients and theirs.
Together with your team, you develop high-performance data pipelines that support financial workflows and internal processes, efficiently converting knowledge into value.
You posses the following collaboration & domain skills:
- A security-first mindset and experience with AI pipelines for both structured and unstructured data
- Strong collaboration skills with development, security, and operations teams
- Energy, ownership and the drive to achieve results together
You optimise semantic search algorithms, develop solutions for data quality assurance, fine-tune and test AI models for specific applications and participate in MLOps activities: managing, deploying and monitoring secure models in production. Finally, you implement secure AI inference solutions that meet the high enterprise standards of the banking and finance sector, collaborating with analysts, developers, and security specialists.
What you bring as an AI Engineer
You combine deep technical expertise with curiosity about financial data and the potential of AI. You have a passion for technology, understand the complexity of financial data, and are always seeking better solutions.
Your technical expertise covers:
- A completed bachelor’s or master’s degree, preferably in IT or Finance
- Experience with modelling and querying semantic context using knowledge graphs (e.g. Neo4j)
- Experience with Retrieval-Augmented Generation (RAG) in vector databases (e.g. Qdrant, Pinecone, Milvus, FAISS)
- Hands-on experience with applying, fine-tuning, and implementing open-source LLMs
- Proficiency in Python and/or Java
- Experience with frameworks such as LangChain and PyTorch
- Familiarity with agentic tools such as GitHub Copilot, Cline, Cursor, or Windsurf
- Experience with cloud ML (e.g. Azure) and container orchestration (Docker/Kubernetes)
