Director of AI & Quant Analytics
Employment Info
JOB DETAILS
Requirements
- Strong modelling depth across classical machine learning used in production decisioning, including forecasting, optimisation, anomaly detection and risk models, with a practical understanding of real-world trade-offs.
- Comfortable partnering with traders/risk, producing decision relevant analytics
- Deep GenAI engineering experience, including: Designing and selecting RAG architectures (hybrid retrieval, re-ranking, metadata strategies).
- Building robust evaluation frameworks covering coverage, failure modes, calibration, drift and regression.
- Implementing safety patterns such as grounding, clear refusal boundaries, deterministic fallbacks and strong auditability.
- A disciplined approach to production delivery, including release engineering, observability, incident response and defining measurable SLOs for models.
- The ability to set technical standards and lead complex AI initiatives end to end.
- A genuine interest in teaching, mentoring and developing junior team members.
- Stack experience should include: Microsoft Azure / Snowpark Container Services, Airflow + Python + Mongo, Kafka, REST API Microservices, React, LangChain / LlamaIndex / AutoGen and snowflake
Responsibilities
- Lead end to end operationalisation of ML + GenAI analytics: Model lifecycle: design → build → validate → release → monitor → retire.
- Production controls: versioning, reproducibility, audit trails, change management.
- Own GenAI technical implementation quality: Retrieval strategy and RAG architecture; define context contracts per use case.
- Define and enforce evaluation strategy (metrics, coverage, red team tests, drift).
- Establish prompt/context release discipline (versioning, rollbacks, safe defaults).
- Deliver analytics that are reliable, scalable, governed in production.
- Maintain WIB CTT ownership boundary, use cases, artefacts, and outcomes (models, evals, dashboards, decision integrations) for our line of business
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