AI Full Stack Engineer

About the role

The Pod Engineer is a hands‑on software engineer who delivers high‑quality software as part of a small, outcome‑focused pod of typically 4–5 engineers. The role operates in an AI‑native delivery model, where AI tools and agents are used across the software development lifecycle (SDLC) to accelerate delivery, improve quality, and reduce manual effort.
Unlike traditional engineering roles, the Pod Engineer is expected to work with AI as a core delivery partner, using AI tools for requirements gathering, design exploration, code generation, testing, documentation, and optimisation—while remaining for correctness, security, and production readiness.

What you’ll be doing

1) Hands‑On Software Delivery
• Develop, build, validate, and deploy software components as part of a delivery pod.
• Contribute production‑quality code across backend, frontend, APIs, or integration layers, depending on the pod’s scope.
• Own features end‑to‑end—from articulating requirements through to deployment and support.
2) AI‑Enabled SDLC Execution
• Leverage AI tools across the SDLC to accelerate acceptance criteria clarity, and enable secure code generation and optimization.
Utilize AI to develop and maintain automation while streamlining documentation and release artefact creation.
• Continuously improve personal and pod‑level productivity through effective AI usage.
3) Engineering Quality & Standards
• Write clean, maintainable, well verified code aligned to the engineering standards.
• Participate actively in code reviews and pair programming.
• Follow secure coding practices and ensure solutions meet performance, reliability, and resilience expectations.
4) Pod Collaboration
• Work closely with the Pod Lead Engineer and other Pod Engineers to deliver outcomes as a cohesive unit.
• Contribute to daily planning, reviews, and retrospectives.
• Support knowledge sharing, experimentation, and continuous improvement within the pod.
5) Learning & Capability Development
• Continuously develop skills in modern software engineering and AI‑assisted development techniques.
• Adopt and share good practices for prompt design, AI evaluation, and safe AI usage.
• Stay current with emerging tools, frameworks, and patterns relevant to AI‑enabled engineering.
6) Governance, Risk & Responsible Engineering
• Adhere to security, data protection, and governance requirements.
• Flag risks, defects, or concerns early—especially those introduced by AI‑assisted development.
• Ensure delivery artefacts are complete, auditable, and fit for production use.

Essential Skills / Experience

• Strong experience as a software engineer delivering production systems.
• Solid foundations in software engineering fundamentals: data structures, APIs, CI/CD, and debugging.
• Hands on expertise in Java with React, PostgreSQL, Neptune DB.
• Strong credentials in Cloud environments like AWS.
• Experience working in Agile delivery environments.
• Practical exposure to AI‑assisted development tools (e.g. Copilot, Amazon Kiro, Claude Code, automated testing, AI‑enabled documentation).

Desirable Skills / Experience

• Experience working in small squads or pods with shared ownership of outcomes.
• Familiarity with cloud platforms and modern delivery pipelines.
• Curiosity and enthusiasm for using AI to improve how software is built