AI Full Stack Lead

About the role

The Pod Lead Engineer is a hands‑on software engineering specialist who leads a small, outcome‑focused delivery pod of 4–5 Pod Engineers. The role combines deep technical contribution with day‑to‑day pod leadership, enabling faster delivery through AI‑assisted / AI‑orchestrated SDLC practices.
Unlike a traditional team lead role, the Pod Lead Engineer operates in an AI‑native delivery model where AI tools and agents are used across the SDLC to accelerate requirements, design, code generation, testing, documentation, and release readiness. The Pod Lead Engineer contribute to technical direction, delivery execution, and engineering quality, while maintaining high standards of security, compliance, and operational excellence.

What you’ll be doing

1) Hands‑On Delivery Leadership (Core)
• Lead a 4–5 engineer pod by building software alongside the team, taking ownership of design, coding, reviews, and integration while breaking work into small, deliverable increments using modern engineering practices (CI/CD, automation, code quality gates).
• Drive frequent delivery of working software by ensuring clear definitions of done, measurable progress, and strong accountability for end‑to‑end delivery outcomes.

2)AI‑Enabled SDLC Execution
• Embed AI across SDLC stages (requirements, design, build, test, deploy, operate) to accelerate solution design, safely generate/refactor code, create tests/automation, and support reviews and documentation.
• Establish pod‑level practices to maximize productivity with clear human accountability for decisions and release readiness, while maintaining required gates (e.g., quality checks, approvals) aligned to organizational standards.

3)Technical Direction & Engineering Quality
• Act as the pod’s technical authority, defining architecture (within guardrails), patterns, component design, and integration approach.
• Own code quality, maintainability, performance, reliability, and security, while establishing pod‑level engineering standards (branching, coding conventions, reviews, CI/CD hygiene).

4)Pod Management (Small Team Leadership)
• Provide day‑to‑day leadership for a 4–5 engineer pod, including task allocation, pairing strategy, prioritization, and removing blockers.
• Coach engineers in AI‑native ways of working (prompting, evaluation, safe AI use) while driving continuous improvement through retrospectives, root cause analysis, and measurable cycle‑time and quality gains.

5)Stakeholder Alignment & Outcome Ownership
• Partner with the Senior Manager, AI Engineering and business/product stakeholders to clarify outcomes, scope, constraints, and acceptance criteria, translating needs into clear technical milestones and deliverable increments.
• Provide transparent updates on progress, risks, dependencies, and mitigation actions to ensure strong alignment and end‑to‑end outcome ownership.

6)Governance, Risk, and Responsible Engineering
• Ensure adherence to security, data protection, and engineering governance standards while proactively managing risks from AI‑assisted development (e.g., licensing, security vulnerabilities, hallucinated code, weak tests).
• Maintain required auditable artefacts (documentation, traceability, approvals) aligned with enterprise expectations for controlled delivery.

Essential Skills / Experience

• 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.
• Expertise in Java or .NET or Python along with UI and backend technologies like React, Flutter, PostgreSQL, Neptune DB.
• Strong credentials in Cloud environments like AWS.
• 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 Skills / Experience

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