AI Engineer - Trading Analytics
- Design, build, and deploy AI-driven analytics for front-office use, including seasonality analysis, correlation, regression, forecasting, and scenario modelling
- Develop reusable and scalable data pipelines using Databricks, PySpark/Spark, Delta Lake, and Unity Catalog, with a focus on performance, reliability, and cost efficiency
- Perform statistical and econometric analysis on large-scale market and fundamentals time-series datasets
- Work directly with traders and analysts to shape requirements, iterate quickly, and clearly communicate analytical insights
- Build and operate LLM-based and agentic workflows, including prompt engineering, retrieval-augmented generation, tool calling, orchestration, and safety guardrails
- Productionize solutions using strong engineering practices, including testing, observability, versioning, documentation, and monitoring
- Hands-on experience with Databricks and Spark, including PySpark, SQL, Delta Lake, and Unity Catalog
- Strong data engineering expertise covering ingestion, modelling, orchestration, and performance tuning
- Solid grounding in statistics, economics, and data science, particularly for market and time-series analysis
- Experience developing and integrating LLM solutions, including agent workflows and retrieval patterns, within data-rich environments
- Familiarity with modern engineering toolchains such as CI/CD, infrastructure as code, ML lifecycle tooling, feature stores, vector databases, and data governance
- Excellent stakeholder and communication skills, with the confidence to work closely with trading teams and deliver value quickly
- Hybrid working model with high-touch collaboration alongside trading teams
- Strong bias toward rapid prototyping, user feedback, and iterative improvement, followed by hardening solutions for production
- Emphasis on operational stability through secure, modern CI/CD practices, including automated testing, quality gates, and reliability across development, security, and operations
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