Senior Machine Learning Engineer (Generative AI)

Role overview

A senior individual-contributor role focused on owning the modelling layer of a hybrid ensemble that converts multilingual, data-scarce market inputs into institutional-grade scores. The work spans gradient boosting, sequence models, and transformer-based NLP, with a strong emphasis on production readiness and external explainability. The engineer will shape how the methodology is published, interrogated, and acted upon by external stakeholders.

Responsibilities

- Design, train, and productionise the hybrid model stack behind the core market and sentiment signals. - Build the retrieval and generation layer so every generated answer traces back to a scored, traceable input. - Own the explainability surface, including SHAP attribution, model cards, and documented invalidation conditions. - Define the evaluation bar through backtesting, drift detection, and robustness tests that appear in published report appendices. - Partner closely with research and data engineering to keep model assumptions aligned with source realities.

Requirements

- Production machine learning experience with genuine end-to-end ownership of a model that drove real downstream decisions. - Fluency across gradient boosting, sequence models, and transformer-based NLP, plus the judgement to choose the right tool — and to decline the wrong one. - A bias toward published, interrogable methodology over unexplained accuracy claims. - Comfort working in multilingual, data-scarce settings where evaluation design is as important as engineering.

Nice to have

- Experience designing models whose outputs are reviewed by institutional due-diligence or risk teams. - Familiarity with retrieval-augmented generation patterns and source-attribution pipelines.