Mid-Senior Full Stack Engineer (Agentic)

THE ROLE

The Senior Agentic Software Engineer delivers secure, scalable and production-ready software using AI-assisted and agentic engineering practices. Work as a human-in-the-loop engineer, directing and validating AI development agents while applying strong engineering judgement across the full software stack.

WHAT YOU'LL DO

• AI-ACCELERATED ENGINEERING

• Deliver enterprise-grade software using the client's enterprise AI platform, GitHub Copilot, Claude and similar tools. Direct AI agents using user stories, Figma designs, business rules, design-system components and engineering standards. Refine AI Skills, markdown instruction files, prompts, workflows and guardrails to improve quality, consistency and delivery velocity. Critically evaluate AI-generated outputs rather than accepting them at face value.

• FULL-STACK DELIVERY

• Design, build and maintain modern frontend experiences, mobile applications, backend services, APIs, integration services, cloud-native components and persistence layers. Move between frontend, backend and platform concerns according to delivery priorities and work across frameworks and languages rather than being constrained by a single technology ecosystem.

• ARCHITECTURE QUALITY

• Contribute to technical decisions and review architecture and implementation choices proposed by AI agents. Ensure solutions are scalable, maintainable, secure, observable and performant. Apply the same engineering standards to AI-generated code as human-written software, while identifying technical debt, architectural drift and quality risks early.

• HUMAN-IN-THE-LOOP ASSURANCE

• Own engineering quality across security and secure coding, performance, reliability, maintainability, accessibility, observability, automated testing, scalability, CI/CD, DevSecOps and technical debt management. Use delivery feedback to improve Skills and instruction files and help establish Agentic Engineering practices that can scale across the programme.

• CONTINUOUS IMPROVEMENT

• Build reusable AI-enabled engineering capabilities that reduce repetitive work and improve future delivery cycles. Translate engineering standards, coding conventions and security requirements into reusable agent guidance. Contribute to operating practices that make Agentic Engineering a scalable way of working across the programme.

• SUCCESS OUTCOMES

• Deliver more business value through AI-enabled engineering than traditional development approaches without compromising enterprise quality. Learn and become productive in new frameworks, languages and platforms quickly. Improve the quality and effectiveness of the AI engineering ecosystem over time, while consistently delivering secure, scalable, maintainable and production-ready software.

• MINDSET

• Work as an AI-native engineer who uses AI as a productivity accelerator while remaining accountable for engineering outcomes. Be framework agnostic, able to move between technologies and become productive quickly as the ecosystem changes. Think beyond individual features to understand the wider system, question agent output, validate assumptions and know when generated code is not good enough.

• DELIVERY STANDARDS

• Treat security, quality, maintainability, testing and performance as non-negotiable. Ensure solutions are observable and resilient, and use engineering feedback to continuously improve the way software is delivered. Share knowledge and good practices with peers and help raise team standards.

EXPERIENCE

Typically 4-8 years of professional software engineering experience, with a clear trajectory toward senior-level capability and proven experience contributing to complex, enterprise-grade software products. Strong full-stack engineering capability across frontend, backend, APIs, cloud services and data, with a solid understanding of distributed systems, API-driven architectures and modern application architecture.

• MOBILE CROSS-PLATFORM

• Commercial experience with modern cross-platform mobile engineering. Experience with React Native and/or Flutter is strongly preferred. Experience with .NET MAUI, Swift, Kotlin or Ionic is advantageous. Demonstrated ability to learn unfamiliar frameworks quickly and make sound implementation decisions within them.

• AI AGENTIC ENGINEERING

• Hands-on experience using AI-assisted software development tools such as GitHub Copilot, Claude or equivalent enterprise platforms. Experience using AI agents for software engineering tasks, including personal projects, and exposure to creating or refining prompts, markdown instruction files or AI-assisted engineering workflows. Understanding how context, instructions, examples and feedback loops influence agent quality, with the ability to identify incorrect assumptions, insecure implementations and superficially plausible but incorrect code.

• TECHNICAL EXCELLENCE

• Working knowledge of software architecture, secure software development, SOLID principles, performance engineering, design patterns, automated testing/TDD, domain-driven design, CI/CD, API design, DevSecOps, distributed systems, observability, cloud-native engineering, accessibility, code quality and technical debt management.

• MINDSET

• AI-native and framework agnostic, using AI as a productivity accelerator while remaining accountable for engineering outcomes. A systems thinker who understands how components fit together, treats security, quality, maintainability, testing and performance as non-negotiable, questions agent output and validates assumptions. Continuously learns, experiments and converts expertise into reusable Skills, instructions and patterns that benefit the wider team. Comfortable with ambiguity as technologies and approaches evolve.

• SOFTWARE ENGINEERING ENVIRONMENT

• Experience working effectively in complex environments with evolving requirements and technology choices, reasoning from engineering principles rather than relying exclusively on framework-specific knowledge.

• ENGINEERING JUDGEMENT

• Ability to review AI-generated code critically, identify failure modes and make sound implementation decisions within unfamiliar frameworks. Able to spot technical debt, architectural drift and quality risks early.

• ADDITIONAL CAPABILITY

• Ability to work across frameworks and languages rather than being constrained by a single technology ecosystem. Experience with established design-system components and engineering patterns is relevant. Contribute to technical decisions with support from senior engineers where needed, while owning the quality of AI-generated solutions.

• CORE ENGINEERING AREAS

• Software architecture, secure software development, SOLID principles, performance engineering, design patterns, automated testing/TDD, domain-driven design, CI/CD, API design, DevSecOps, distributed systems, observability, cloud-native engineering, accessibility, code quality and technical debt management.

DOMAIN ADDITIONAL EXPERIENCE

Digital banking is preferred, while strong enterprise engineering experience is essential. Banking or broader financial services experience is a nice to have, as is experience with enterprise design systems and reusable UI component libraries.

PRODUCT DELIVERY

Experience translating Figma designs and product requirements into production software is beneficial. Experience in product-led, agile or large-scale digital transformation programmes is also advantageous. Early experience mentoring junior developers or reviewing code across a team is useful, as is exposure to engineering standards, governance practices or developer enablement work.

SUCCESS

Deliver more business value through AI-enabled engineering without compromising enterprise quality. Learn and become productive in new frameworks, languages and platforms quickly. Improve the quality and effectiveness of the AI engineering ecosystem over time while exercising strong engineering judgement when validating AI-generated designs and code.

Consistently deliver