Lead Software Engineer (C# / .NET / REACT)

Lead Software Engineer (C# / .NET / REACT) is required by highly successful, fast-growing, and international organisation.

They are seeking a pragmatic, impact-driven Lead Software Engineer to help evolve and extend their platform while guiding technical direction and supporting team development. This role centres on solving real-world problems, making thoughtful technical decisions that balance business impact with engineering trade-offs. You will work closely with Product Managers, Designers, and engineers to take ideas from inception through to production, ensuring they build the right solutions for their customers. You will act as both a senior technical contributor and a product-minded engineer, someone who doesn’t just implement requirements but actively shapes them. You will challenge assumptions, ask the right questions, and help ensure they are solving meaningful problems in the most effective way. You will contribute to technical strategy by identifying opportunities to improve system reliability, performance, and user experience within their current architecture, while also supporting the evolution of their strategic platform. This role will play a key part in driving the adoption of AI across engineering, leveraging emerging technologies and embedding AI-native workflows to improve how they design, build, and deliver software.

This role is predominantly hands-on, with around 20–30% of time on technical leadership: architecture, mentoring, people management, and shaping how the team works.

Key Responsibilities

• Help the team move faster by adopting AI-native workflows and the shift to an agentic development lifecycle, without compromising the architectural qualities that make their platform reliable, scalable, and maintainable over the long term.
• Define guardrails, review practices, and validation gates that hold AI-generated code to their standards for security, reliability, and cost-efficiency (FinOps). These are first-class concerns in agentic pipelines, not afterthoughts.
• Design, build, and operate software across their platform, applying the same rigour to AI-augmented work as to anything else you ship.
• Embed deeply with Product and Design from the earliest stages of discovery, using rapid prototyping and iteration to compress the gap between idea and validated solution.
• Act as a Tech lead, taking accountability for the development and delivery of a feature from shaping through to production. This means partnering with Product and Design on the approach, breaking the work down, coordinating the engineers contributing to it, removing blockers, and being the person who knows the state of the feature at any point.
• People management responsibilities for 1-3 engineers, holding regular 1:1s, performance reviews, coaching, and mentoring.
• Take accountability for what your team delivers, from shaping through to production. You are the person who knows the state of the work at any point.
• Serve as a critical technical voice, stress-testing AI-generated solutions, surfacing second-order risks, and ensuring the team builds the right thing, not just the fast thing.
• Shape platform architecture with an architectural lens, contributing to long-term decisions about how AI tooling, LLM integrations, and human-in-the-loop controls evolve across the stack.
• Continuously identify and exploit opportunities to improve performance, reliability, and user experience, using observability and analysis to find signals in noisy systems.
• Navigate confidently across legacy and greenfield contexts, applying AI tooling pragmatically to modernise where it matters most.
• Set the engineering standard, demonstrating through your own work what excellent looks like when much of the code is AI-generated.
• Grow the team's capability and confidence with AI-native practices, coaching more junior engineers to think critically about model outputs, prompt design, and the boundaries of automation.
• Define and evolve best practices for code quality, testing, documentation, and delivery in a world where much of the first draft is AI-generated.
• Evolve CI/CD pipelines to incorporate agentic workflows, automated testing, AI-assisted code review, and intelligent deployment gates.