Senior Java Engineer - AI Enablement

Overview

Senior Java Engineer - AI Enablement at IG Group. Join a FTSE
250 Fin Tech company delivering platforms for trading stocks, leveraged products, Futures & Options and Crypto. This is a hands-on engineering role focused on delivering modernised services with AI-enabled development practices.

• Service Modernisation (50%)
  • Modernise legacy services using AI to analyse codebases, understand dependencies, and extract clean APIs
  • Work on high-impact legacy services that block divisional delivery speed
  • Implement strangler fig patterns and other proven migration approaches
  • Deliver modernised services with comprehensive tests, documentation, and multi-instance deployment capabilities
  • Ship results quickly with monthly milestones
  • Use AI to accelerate every phase: analysis, refactoring, testing, documentation
  • Hand off modernised services to Platform Services or divisions with clear ownership
  • Demonstrate measurable improvements: faster APIs, better performance, higher reliability
• AI Implementation & Automation (50%)
  • Build AI-powered development infrastructure, including Model Context Protocol (MCP) servers for service discovery, dependency mapping, and architecture compliance
  • Create AI-assisted CI/CD pipelines with automated code review, security scanning, and test generation
  • Build automation using Claude Code, Git Hub Copilot, and LLM APIs
  • Develop reusable AI tooling that other engineers can adopt
  • Demonstrate AI-first development: use AI for coding tasks, refactoring, test creation, and documentation
  • Train teams during service handoffs on AI-enabled workflows you've built
  • Demonstrate when to use AI vs human judgement

• Backend Engineering: 8+ years of software engineering with Java backend expertise
• Experience modernising production systems at scale
• Strong API design and microservices architecture knowledge
• Understanding of strangler fig patterns, service decomposition, and legacy migration strategies
• AI/LLM Implementation (Critical Differentiator): hands-on experience building with Model Context Protocol (MCP)
• Demonstrated use of Claude Code, Git Hub Copilot, or similar AI development tools in production work
• Experience implementing AI in CI/CD pipelines (code review, testing, security scanning)
• Built agentic AI solutions or AI-powered automation tools
• Understanding of prompt engineering, model selection, and LLM capabilities/limitations
• Proven AI impact: measurable productivity improvements using AI in development; AI-assisted refactoring, test generation, or documentation at scale

• Delivery-focused:
  Ship working modernised services
• Hands-on:
  Write code daily
• Pragmatic:
  Use AI to go faster, not to be clever
• Teacher through doing:
  Others learn by seeing your PRs and shipped work
• Measurement-driven:
  Track your AI productivity gains and share data
• Collaborative:
  Work across divisions and time zones professionally