Graduate Infrastructure Engineer: Fast-Track in Trading Tech

targetjobs UK; Greater London; On-site; GBP 35,000 - 52,000; Full time

Benefits offered by this job

25 days annual leave

Learning and development budget

Daily meals in London office

Private medical and dental cover

Relocation assistance

Cycle to work

Job summary

Maven Securities is a leading proprietary trading firm offering a structured graduate infrastructure engineering programme in London. You will start with a training phase across multiple business areas, then join a specialised technical team to build deep expertise.

The programme emphasises hands-on experience, exposure to AI technologies, and a pathway to long-term development with a dedicated learning budget and on-site training resources.

Qualifications

• Final-year undergraduate or master's student in Networking, Network Security, Computer Science or Software Engineering.
• Willingness to adapt to a dynamic, high-paced environment.
• Willingness to adopt and utilise emerging AI technologies.

Responsibilities

• Keep trading-critical Linux systems running at required speed; build and tune Linux estates and kernel/NIC tuning for low-latency workloads.
• Operate and harden the network that market data and orders travel over with Arista/Cisco/Juniper gear; monitor latency and routing.
• Join on-call rotations; manage P1 incidents end-to-end during market hours and resolve issues quickly.
• Build observability using Prometheus, Grafana and the logging stack; contribute to dashboards and alerts used daily by infra teams.
• Attend regular check-ins with Infrastructure and Emerging Talent teams.

Skills

Adaptability

AI awareness

Education

Networking/CS degree

Tools

Linux (RHEL/Ubuntu)

Networking hardware (Arista/Cisco/Juniper)

Job description

Maven Securities is a leading proprietary trading firm offering a structured graduate infrastructure engineering programme in London. You will start with a training phase across multiple business areas, then join a specialised technical team to build deep expertise.

The programme emphasises hands-on experience, exposure to AI technologies, and a pathway to long-term development with a dedicated learning budget and on-site training resources.