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Description:

AI Engineer (Trading Analytics & Data Engineering)

About the role

We're hiring an AI Engineer with a strong data engineering foundation and excellent communication skills—ideally with commodity or financial trading experience. You'll partner with traders and trading analysts to rapidly build AI‑powered analytics over market pricing and fundamentals data, using Databricks and Spark to deliver value at speed.

What you'll do

• Design and ship AI‑driven analytics for front‑office use (seasonality, correlation, regression, forecasting, scenario modelling).
• Build reusable and scalable data pipelines in Databricks (PySpark/Spark, Delta/Unity Catalog), optimizing cost, reliability, and performance.
• Run statistical/econometric analyses on large datasets (e.g., market & fundamental time series data).
• Collaborate directly with traders/analysts—translate ambiguous questions into shippable solutions; communicate insights clearly.
• Implement LLM/agentic workflows: prompt engineering, LangGraph orchestration, MCP integrations, tool calling, retrieval, and guardrails.
• Productionize solutions with testing, observability, versioning, and documentation.

What you'll bring

• Hands‑on Databricks + Spark expertise (PySpark, SQL, Delta, Unity Catalog). Proven data engineering skills (ingestion, modelling, orchestration, performance tuning).
• Strong statistics/economics/data science fundamentals for market time‑series.
• Experience building LLM solutions (prompting, retrieval, agent flows; LangGraph, MCP) and integrating with trading data/services.
• Experience with CI/CD, Terraform, MLflow/feature stores, vector DBs, and governance (PII handling, data lineage).
• Excellent stakeholder skills; able to work on‑desk with traders/analysts and deliver fast.

Nice to have

• Background in commodity or financial trading.
• Familiarity with market microstructure, supply‑demand fundamentals, risk management.
• Ways of working
• Hybrid; high‑touch collaboration with trading teams.
• Bias to prototype fast, iterate with users, and harden to production.
• Maintain operational stability of production pipelines using secure, modern CI/CD engineering practices — with automated testing, quality gates, and built‑in reliability across development, security, and operations.