You’ll join a dedicated team responsible for the development, implementation, and support of AI/GenAI tooling to enable scalable, efficient, and reliable validation and risk oversight.
- Collaborating to develop tools for validation, testing, explainability, and monitoring.
- Supporting validation activities by developing prototype pipelines and frameworks for testing AI/GenAI models.
- Building and maintaining reusable code libraries to automate model documentation, validation, and risk assessments.
- Driving technical innovation and continuous improvement of the validation tooling environment.
- Developing benchmark testing templates and reporting dashboards to streamline assurance across use cases.
- Monitoring emerging technologies (e.g., LLMOps, RAG pipelines, agent toolkits) to future‑proof the validation and tooling landscape.
- Supporting knowledge sharing, collaboration, and tooling governance across stakeholders.
Requirements
- 3+ years professional experience working in AI Model Development / Validation, or a similar quantitative role within financial services or other regulated industries; or a recent relevant PhD
- A numerate degree or equivalent experience (e.g., Data Science, Statistics, Mathematics, Computer Science, or Physics)
- Strong analytical and problem‑solving skills with the ability to critically evaluate complex AI systems and models
- Excellent written and verbal communication skills, with an ability to communicate complex quantitative concepts clearly to non‑technical customers
- Ability to work proactively and independently, manage time optimally, and deliver high‑quality outputs within tight timelines
- Proficiency in Python programming and experience using AI‑specific frameworks or libraries such as PyTorch, TensorFlow, LangChain, LlamaIndex, or similar tools; including SQL
- Hands‑on experience or strong theoretical knowledge of GenAI techniques and methodologies
- Familiarity with cloud AI platforms such as GCP Vertex AI and BigQuery, Azure AI, or similar enterprise‑level AI deployment environments
- Experience and knowledge of regulatory requirements and frameworks relevant to AI, such as the EU AI Act, GDPR, SS1/23, and standard processes in AI ethics and governance
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