The global private Bank Core Technology AI Team is delivering production quality AI solutions to multiple lines of business in Global Private Bank. We are designing solutions in environments where trust and explainability are critical.
Requirements
- 5+ years of experience delivering ML/AI solutions in production, including deep familiarity with Python-based ML/LLM stacks (e.g., PyTorch, Hugging Face, Scikit-learn)
- Strong track record developing and scaling LLM or NLP systems—document extraction, QA, summarization, embeddings, etc.
- Practical understanding of model deployment, evaluation, and lifecycle management (not just experimentation)
- Familiarity with multi-agent systems or chaining LLMs with traditional systems
- Exposure to model monitoring, fairness, explainability, or AI governance
Responsibilities
- Design, develop, and deploy LLM-based and traditional ML solutions that enhance real-world business processes (e.g., document understanding, summarization)
- Serve as a bridge between data science, engineering, and business—engaging directly with stakeholders to frame problems, interpret results, and drive adoption
- Deeply understand business data and workflows, uncover actionable insights, and surface opportunities for transformation using AI
- Contribute to best practices around model validation, evaluation, safety, and responsible AI
- Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production
Other
- Collaboration across business and tech is core to our culture
- Excellent communication skills; ability to explain complex technical concepts to non-technical stakeholders and senior leaders
- Comfortable working in ambiguous problem spaces and navigating across technical and business domains
- Detail-oriented, thoughtful, and mission-driven—you care about making things work, not just making things interesting
- Experience working in financial services, especially regulated environments
