Full Stack Lead - AI

The candidate will have responsibilities across the following functions:

HandsOn Delivery Leadership (Core):

• Lead delivery for a pod (4-5 engineers) by building software alongside the team, owning design, code, reviews, and integration.
• Break down outcomes into small, deliverable increments and drive execution through modern engineering practices (CI/CD, automated testing, code quality gates).
• Ensure the pod delivers working software frequently, with a clear definition of done and measurable progress.

AI-Enabled SDLC Execution:

• Embed AI tools and techniques across delivery stages (requirements, design, build, test, deploy, operate), using AI to: accelerate solution exploration and design options, generate and refactor code safely, create unit tests and automation, perform code review support and documentation.
• Set pod-level working patterns that maximise productivity while ensuring human accountability for final decisions and release readiness.
• Maintain gate practices where needed (e. g., quality checks, approvals), consistent with the organisational standards.

Technical Direction and Engineering Quality:

• Act as the pod's technical authority: architecture decisions (within guardrails), patterns, component design, and integration approach.
• Own code quality, maintainability, performance, reliability, and security posture for the pod's deliverables.
• Establish engineering standards inside the pod (branching, coding conventions, review practices, test expectations, CI/CD hygiene).

Pod Management (Small Team Leadership):

• Provide day-to-day leadership for 4-5 Pod Engineers: task allocation, pairing strategy, unblockers, and prioritisation.
• Coach engineers in AI-native ways of working, including good prompting habits, evaluation discipline, and safe use of AI tooling.
• Drive continuous improvement: retrospective actions, root cause analysis, and measurable cycle time/quality improvements.

Stakeholder Alignment and Outcome Ownership:

• Work closely with the Senior Manager, AI Engineering and business/product partners to clarify outcomes, scope, constraints, and acceptance criteria.
• Translate business needs into technical work with clear milestones and demonstrable increments.
• Provide transparent progress updates, risks, dependencies, and mitigation actions.

Governance, Risk, and Responsible Engineering:

• Ensure delivery adheres to security, data protection, and engineering governance expectations.
• Proactively manage risks introduced by AI-assisted development (e. g., licensing concerns, security vulnerabilities, hallucinated code, weak tests).
• Maintain auditable artefacts where required (documentation, traceability, approvals), aligning to enterprise expectations for controlled delivery.

This Role Different from a Traditional Tech Lead:

• You lead a small pod (4-5 engineers) rather than a large team.
• You are expected to be handson and contribute to the codebase of the pod.
• Become accountable for delivery outcomes, not primarily people management.
• You use AI as a core delivery engine across the SDLC, not as an occasional productivity add-on.

The core requirements for the job include the following:

Essential:

• Strong experience as a senior software engineer / technical lead/engineering specialist delivering production systems.
• Solid engineering fundamentals: API design (REST), data modelling, testing strategy, CI/CD, secure coding, reliability practices.
• Proven expertise in Java or Python along with UI and backend technologies like React, Flutter, PostgreSQL,

Neptune DB:

• Strong credentials in Cloud environments like AWS.
• Proven experience leading small delivery groups (e. g., squad/pod/team) and delivering iteratively.
• Practical experience using AI development tools (Copilot, Amazon CodeWhisperer, Claude Code, agent workflows,
• automated test generation, AI-assisted documentation).

Desirable:

• Experience working in AI-assisted / agentic delivery models or rapid delivery pods/cohort models.
• Familiarity with governance practices that support quality and assurance in delivery environments (e. g., gate reviews, readiness checks).

Success Measures (What Good Looks Like):

• Pod consistently delivers working increments on schedule, with strong engineering hygiene.
• Reduced cycle time and higher throughput via AI-enabled practices, without compromising quality.
• High code quality, test coverage, and production stability for pod-owned components.
• Pod Engineers demonstrate increasing capability in AI-native SDLC practices under your coaching.
• Clear stakeholder confidence through predictable delivery, transparent risks, and disciplined execution.