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.
• Provide transparent progress updates, risks, dependencies, and mitigation actions.
• Ensure delivery adheres to security, data protection, and engineering governance expectations.
• Proactively manage risks introduced by AI-assisted development, including licensing concerns, security vulnerabilities, hallucinated code, and weak tests.
• Maintain auditable artifacts where required, including documentation, traceability, and approvals, aligned to enterprise expectations for controlled delivery.

Requirements:

• Strong experience as a senior software engineer, technical lead, or engineering specialist delivering production systems.
• Solid engineering fundamentals, including API design (REST), data modeling, testing strategy, CI/CD, secure coding, and reliability practices.
• Proven expertise in Java or Python along with UI and backend technologies such as React, Flutter, PostgreSQL, and Neptune DB.
• Strong credentials in cloud environments such as AWS.
• Proven experience leading small delivery groups such as squads, pods, or teams and delivering iteratively.
• Practical experience using AI development tools such as Copilot, Amazon CodeWhisperer, Claude Code, agent workflows, automated test generation, and AI-assisted documentation.

Nice To Have:

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