Pod Lead Engineer (AI Native Software Engineering Specialist)

What you will be doing (Role Accountabilities)

1) Hands‑On 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 clear definition of done and measurable progress.

2) 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 organizational standards.

3) Technical Direction & 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).

4) 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.

5) Stakeholder Alignment & 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.

6) 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.

What Makes 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 hands‑on and contribute to the codebase of the pod. Become accountable for delivery outcomes and not primarily people management.
• You use AI as a core delivery engine across SDLC, not as an occasional productivity add‑on.

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 Kiro, 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).