Senior Java Engineer - AI Enablement
City of London - United Kingdom, UKОсновні характеристики вакансії
Сервер: Java / .NET / Node / Python
Потрібні спеціалісти - старший/експерт
Повний робочий день
Вступне навчання
Description
Hello, we're IG Group. We are an FTSE250 FinTech company who run mobile, web and desktop platforms that help our clients trade stocks & shares, leveraged products, Futures & Options and Crypto.
Proven AI Impact
Achieved measurable productivity improvements using AI in development
Implemented AI-assisted refactoring, test generation, or documentation at scale
Experience with AI code analysis and automated remediation
Track record of shipping production systems built with AI assistance
Backend Engineering
8+ years 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
The Perks
Competitive salary
Flexible Benefits Package on top of your salary (12%)
Private medical cover for you and your family
Life insurance
Contribution to gym memberships
25 Days holiday, with 1 additional day off to celebrate your Birthday & 2 additional days off a year for voluntary work (28 in total
The option to buy or sell holiday days.
Unlimited access to the LinkedIn Learning Platform
A comprehensive global and local onboarding process
Employee-led LGBTQ+, Women’s, Black and Parents & Carers networks with an annual budget for organising events & projects that foster an open, diverse and inclusive culture
Enhanced primary (maternity), secondary (paternity), and shared parental pay and leave, as well as a range of support and benefits for parents
Option to participate and create ESG initiatives based on IG Brighter Future Fund
Ship Results Quickly
Complete service modernisations in fast cycles with monthly milestones
Use AI to accelerate every phase: analysis, refactoring, testing, documentation
How we work
Lead and Inspire: Drives trust, alignment, and enthusiasm
Think Big: Focus on the problems that most impact commercial outcomes
Champion the client: Understand and prioritise client's needs
Deliver at pace: Push for fast, sustainable growth;
Raise the bar: Take ownership, be accountable and share feedback
Service Modernisation (50%)
Hand off modernised services to Platform Services or divisions with clear ownership
Demonstrate measurable improvements: faster APIs, better performance, higher reliability
Demonstrate AI-First Development
Use AI for all coding tasks: refactoring, test creation, documentation, debugging
Achieve measurable and significant productivity improvements through AI integration
Document patterns and share learnings through your work
Train teams during service handoffs on AI-enabled workflows you've built
Demonstrate when to use AI vs when human judgement is critical
Modernise Legacy Services Using AI
Use 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
Build AI-Powered Development Infrastructure
Implement 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, GitHub Copilot, and LLM APIs
Develop reusable AI tooling that other engineers can adopt
AI/LLM Implementation (Critical Differentiator)
Hands-on experience building with Model Context Protocol (MCP)
Demonstrated use of Claude Code, GitHub 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
Culture & Leadership Style – What Matters Most
Delivery-focused: Ship working modernised services, don't just build tools
Hands-on: Write code daily, don't just architect or advise
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