Principal Agentic Software Engineer
First seen by Alion on Oct 30, 2026.
We are looking for exceptional Principal Agentic Software Engineers to join a major digital banking transformation programme and help establish a new standard for enterprise software delivery.
This role represents the evolution of the Principal Software Engineer. Rather than focusing primarily on manually producing code, you will use enterprise AI engineering capabilities, development agents, AI Skills and structured instruction files to accelerate delivery while maintaining rigorous engineering standards.
You will operate as the senior human-in-the-loop: directing agents, reviewing their outputs, making architecture decisions, identifying failure modes, improving the instructions that guide future work, and ensuring every solution is secure, performant, maintainable and production-ready.
Technology choices will continue to evolve. Today's implementation may use .NET MAUI, while tomorrow's may use React Native, Flutter, Ionic or another emerging framework. Success therefore depends less on allegiance to a specific framework and more on deep engineering capability, strong systems thinking and an ability to learn and apply new technologies rapidly.
Agentic Engineering is the practice of directing, validating and continuously improving autonomous or semi-autonomous AI software development agents to deliver enterprise-grade software through human engineering judgement.
Duties & ResponsibilitiesAI-Accelerated Engineering
- Deliver enterprise-grade software using advanced AI-assisted and agentic engineering practices.
- Use the client's enterprise AI platform alongside tools such as GitHub Copilot and Claude to accelerate implementation.
- Direct AI development agents using complete product inputs such as user stories, Figma designs, business rules, design-system components and engineering standards.
- Continuously refine AI Skills, markdown instruction files, prompts, workflows and engineering guardrails to improve quality, consistency and delivery velocity.
- Create reusable AI-enabled engineering capabilities that reduce repetitive work and improve future delivery cycles.
- Evaluate AI-generated outputs critically rather than accepting them at face value.
Full Stack, End-to-End Delivery
- Design, build and maintain modern frontend experiences, mobile applications, backend services, APIs, integration services, cloud-native components and persistence layers.
- Move comfortably between frontend, backend and platform concerns according to delivery priorities.
- Work across frameworks and languages rather than being constrained by a single technology ecosystem.
- Use established design-system components and engineering patterns to deliver consistent, high-quality experiences.
Architecture & Engineering Leadership
- Act as a senior technical authority within AI-assisted delivery.
- Validate architecture and implementation choices made or proposed by AI agents.
- Ensure solutions remain scalable, maintainable, secure, observable and performant.
- Review AI-generated code with the same or greater rigour applied to human-written software.
- Identify architectural drift, technical debt and hidden quality risks early.
- Coach engineers in modern Agentic Engineering practices and raise the technical bar across the team.
Quality Engineering & Human-in-the-Loop Assurance
Own the engineering quality of AI-generated solutions, including:
- Security and secure coding
- Performance and optimisation
- Reliability and resilience
- Maintainability and code quality
- Accessibility
- Observability
- Automated testing and testability
- Scalability
- CI/CD and DevSecOps
- Technical debt management
Continuous Improvement of the Agentic Engineering System
- Use code review and delivery feedback to improve Skills and instruction files with every iteration.
- Translate engineering standards, coding conventions, security requirements and recurring review feedback into reusable agent guidance.
- Measure where AI is effective, where human intervention is required and where workflows can be improved.
- Help define the operating model, governance and engineering practices required to scale Agentic Engineering across the programme.
- Contribute to making this way of working the future standard for software engineers.
Software Engineering
- Typically 8+ years of professional software engineering experience, with demonstrable Principal-level engineering capability.
- Proven experience designing and delivering complex, enterprise-grade software products.
- Strong full stack engineering capability across frontend, backend, APIs, cloud services and data.
- Strong understanding of distributed systems, API-driven architectures and modern application architecture.
- Evidence of operating effectively in complex environments with ambiguity and rapidly changing technology choices.
- Ability to reason from engineering principles rather than relying exclusively on framework-specific knowledge.
Mobile & Cross-Platform Engineering
- Strong 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 architecture 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 directing AI agents to perform meaningful software engineering tasks across the delivery lifecycle.
- Experience creating or refining AI Skills, prompt libraries, markdown instruction files, coding agents or reusable engineering workflows.
- Strong understanding of how context, instructions, examples, constraints and feedback loops influence agent quality.
- Ability to recognise AI failure modes including incorrect assumptions, insecure implementations, architectural inconsistency and superficially plausible code.
You should be fluent enough in the following disciplines to guide and challenge AI agents effectively:
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
Technical debt management
Advantageous
- Banking or broader financial services experience.
- Experience working with enterprise design systems and reusable UI component libraries.
- Experience translating Figma designs and product requirements into production software.
- Experience in product-led, agile or large-scale digital transformation programmes.
- Experience mentoring senior engineers and influencing architecture across multiple teams.
- Experience establishing engineering standards, governance and reusable developer enablement capabilities.
Send CV to [email protected]