Design and deliver AI-driven analytics for front-office use, including forecasting, seasonality, correlation, regression, and scenario modelling
Build scalable, reusable data pipelines using Databricks (PySpark/Spark, Delta, Unity Catalog), optimizing performance, cost, and reliability
Develop real-time and near real-time data solutions to support trading and reporting needs
Translate complex trading problems into prototypes and MVPs, iterating rapidly based on feedback
Partner closely with traders and analysts to understand requirements and communicate insights effectively
Implement LLM and agent-based workflows (prompt engineering, orchestration, retrieval, tool usage, and guardrails)
Perform statistical and econometric analysis on large-scale time-series datasets
Productionize solutions with robust testing, observability, CI/CD pipelines, and documentation
Enable reporting and data access via tools such as Power BI and similar platforms

Strong hands-on experience with Databricks and Spark (PySpark, SQL, Delta Lake, Unity Catalog)
Proven data engineering expertise (data ingestion, modelling, orchestration, performance tuning)
Solid foundation in statistics, econometrics, or data science—particularly with market time-series data
Experience building AI/ML and LLM-based solutions (prompting, retrieval, agent workflows)
Proficiency in Python and modern data/ML tooling (e.g., MLflow, feature stores, vector databases)
Familiarity with CI/CD, Terraform, and production-grade engineering practices
Excellent communication and stakeholder management skills, with the ability to work directly with front-office users

Hybrid model with close collaboration alongside trading teams
Fast-paced, iterative delivery: prototype quickly, refine with users, and scale to production
Strong focus on engineering excellence, including automated testing, governance, and operational reliability

AI Engineer – Trading Analytics, Data Platforms

Key skills