Senior Market Data Engineer – AI‑Driven Front Office

Citigroup Inc.; Greater London; Hybrid; GBP 110,000 - 170,000; Full time

Benefits offered by this job

Hybrid work model (up to 2 days WFH)

Competitive base salary

Private medical insurance

Pension plan

Parental leave

Learning & development resources

Job summary

Citi London is seeking a Senior Software Engineer to help design and deliver low-latency market data infrastructure for global commodities trading desks. You will own architecture and implementation of scalable services across Energy, Metals, and Agriculture, with touchpoints in FX, Rates, and Equities.

The role collaborates with Front Office Traders, MQAs, Market Risk, and Operations to embed Generative AI and Agentic AI workflows into development, testing, and production cycles, while driving

Qualifications

• Extensive backend programming in Java, Python or C#/.NET.
• Strong experience with distributed systems and messaging (Kafka, RabbitMQ, gRPC).
• Proficiency in relational and time-series databases (PostgreSQL, Redis, InfluxDB).
• Familiarity with AI tooling and GenAI workflows for engineering.

Responsibilities

• Architect and optimize low-latency market data pipelines.
• Partner with front office traders and quants to translate requirements into production systems.
• Lead DevOps practices and ensure observability, CI/CD, and reliability.
• Prototype AI-enabled data quality and anomaly detection in trading workflows.

Skills

Java

Python

C#/.NET

Distributed Systems

Data Storage

DevOps & Cloud

AI Tooling

Education

Bachelor's degree

Master's degree

Tools

Kafka

Docker

Kubernetes

OpenShift

GitHub Actions

Job description

Citi London is seeking a Senior Software Engineer to help design and deliver low-latency market data infrastructure for global commodities trading desks. You will own architecture and implementation of scalable services across Energy, Metals, and Agriculture, with touchpoints in FX, Rates, and Equities.

The role collaborates with Front Office Traders, MQAs, Market Risk, and Operations to embed Generative AI and Agentic AI workflows into development, testing, and production cycles, while driving