Our client is a global insurance leader running complex, business-critical systems at enormous scale - and it is rebuilding how engineering works. Its technology organisation is moving to an AI-native model across the entire product lifecycle : from idea and design through development, testing, deployment and operations.
The foundations are already in place: AI-assisted and agentic software engineering, context and harness engineering, agent-based test automation, AI-driven reverse engineering of legacy applications and an AI-native product development lifecycle. Its strongest teams already perform at the level of good teams at major technology companies. The challenge now is scaling that across a global engineering organisation - and that is where you come in.
This is neither a conventional individual-contributor role nor a traditional line-management role. It is a hands-on technical leadership position for engineers who can shape complex solutions, introduce new ways of working and act as multipliers across delivery teams.
You will work inside real delivery teams on legacy modernisation, systems integration and data-intensive products - not run workshops from the sidelines. At Principal level, you may own the technical direction of a major business domain.
Lead the technical design and evolution of complex, business-critical enterprise applications
Design robust, scalable solution architectures grounded in practical engineering
Review application portfolios, legacy architectures and integrations, and find ways to modernise and simplify
Translate business requirements, customer journeys and product needs into actionable technology solutions
Guide decisions on integration patterns, APIs, distributed systems, cloud infrastructure and modern application architectures
Introduce and embed AI-native software engineering practices within delivery teams
Apply agentic AI tooling to development, legacy system understanding, test automation and engineering workflows
Use and evaluate context engineering, harness engineering, reverse engineering and autonomous engineering agents
Experiment with emerging AI development tools and judge what actually works on enterprise problems
Identify barriers to AI adoption and build practical ways to overcome them
Act as a hands-on mentor, working directly alongside developers to demonstrate better engineering approaches
Lead architecture discussions, solution reviews and knowledge-sharing sessions
Influence engineering standards and technical decisions without relying on formal line-management authority
Help teams become more independent, confident and technically mature
Partner with product leaders, engineering managers, architects and business stakeholders
Explain complex technical options so the business can make informed decisions
Build credibility with both engineering practitioners and technology leadership
A strong track record in modern software engineering and delivery of complex systems
Practical solution architecture capability - you can explain major architectural decisions and trade-offs in detail
Current hands-on credibility through delivery, prototyping, experimentation or substantive technical work
Real, applied experience with AI-assisted and agentic software development - with evidence of how it improved workflows, quality or outcomes
A product engineering mindset: you connect architecture decisions to customer journeys, business processes and outcomes
Demonstrable ability to coach, mentor and upskill other engineers
Strong communication and a collaborative, curious, low-ego working style
Formal people-management experience is not required. The client cares about evidence of ability and impact more than years of experience or titles.
Insurance technology experience (commercial, claims, life, policy administration or high-net-worth) - an advantage, not a requirement
Banking, payments, capital markets or other regulated financial-services engineering
Large-scale enterprise modernisation, legacy transformation or complex systems integration
AI-enabled testing, codebase analysis or developer productivity tooling
You build technical credibility quickly within your assigned teams
You introduce AI-native approaches with clear, measurable value to delivery
Teams get stronger rather than depending on you
At Principal level, you take meaningful accountability for the technical direction of a significant domain
Scale: enterprise systems and complex products with global reach
Complexity: hard modernisation, integration and architecture problems
Impact: visible influence over substantial domains, not a narrow slice of a giant tech org
Stability: an established, financially strong employer
Purpose: make AI-native engineering a real enterprise operating model, not a pilot
Work mode: Hybrid - approximately 3 days per week in the office
Level: Staff Engineer or Principal Engineer
Interview process: typically 3-5 stages, including a live, case-based solution architecture discussion. No on-the-spot coding test.
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