Pod Lead Engineer - AI - Native Software Engineering

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 and 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 understanding, design, code generation, testing, documentation, and release readiness. The Pod Lead Engineer is accountable for technical direction, delivery execution, and engineering quality while maintaining high standards of security, compliance, and operational excellence.

Responsibilities:

• Lead delivery for a pod of 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, including CI/CD, automated testing, and code quality gates.
• Ensure the pod delivers working software frequently, with a clear definition of done and measurable progress.
• Embed AI tools and techniques across delivery stages, including requirements, design, build, test, deploy, and operate.
• Use AI to accelerate solution exploration and design options, generate and refactor code safely, create unit tests and automation, and support code reviews and documentation.
• Set pod-level working patterns that maximize productivity while ensuring human accountability for final decisions and release readiness.
• Maintain gate practices where needed, including quality checks and approvals, consistent with organizational standards.
• Act as the pod's technical authority for 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, including branching, coding conventions, review practices, test expectations, and CI/CD hygiene.
• Provide day-to-day leadership for 4-5 pod engineers through task allocation, pairing strategy, unblockers, and prioritization.
• Coach engineers in AI-native ways of working, including effective prompting, evaluation discipline, and safe use of AI tooling.
• Drive continuous improvement through retrospective actions, root cause analysis, and measurable cycle-time and quality improvements.
• Work closely with the senior manager, AI engineering, and business and product partners to clarify outcomes, scope, constraints, and acceptance criteria.
• Translate business needs into technical work with clear milestones and demonstrable increments.