Accountabilities:

End-to-End Product Engineering: Lead engineering initiatives from problem definition and technical discovery through architecture, implementation, production deployment, and post-launch performance evaluation.
Technical Architecture and Decision-Making: Own the technical approach for assigned initiatives, including system architecture, data models, integrations, rollout strategies, observability, and rollback plans. Make independent technical decisions while involving architects in significant architectural choices.
AI-Driven Software Development: Leverage AI coding agents such as Claude Code, Cursor, Codex, and equivalent tools to accelerate implementation. Design effective prompts, agent workflows, and guardrails to ensure AI-generated code meets production engineering standards.
Implementation Quality and Reliability: Establish and maintain robust testing strategies, evaluations, code review processes, and validation mechanisms to ensure AI-assisted implementations are secure, reliable, scalable, and production-ready.
Cross-Functional Collaboration: Work closely with Product Managers to define scope, prioritize requirements, and evaluate trade-offs, while partnering with designers on user experience decisions and prototyping.
Shared Codebase Collaboration: Coordinate with engineering peers across multiple initiatives, maintaining consistency, sharing technical knowledge, and contributing to the overall quality of the engineering organization.
Business and Product Outcomes: Take ownership of the success metrics associated with each initiative, such as conversion rates, customer retention, service professional funnel performance, and unit economics.
Performance Measurement: Collaborate with Product Managers to review initiative results approximately 2–4 weeks after launch, analyze performance data, and identify whether the intended business outcomes were achieved.
Production Excellence: Maintain high standards for software correctness, security, performance, observability, and user experience across all customer- and professional-facing products.
Deployment and Risk Management: Implement appropriate testing, staged rollouts, monitoring, and review gates to minimize regressions and ensure safe production releases.
AI Workflow Innovation: Continuously improve AI agent workflows, prompts, evaluation frameworks, and review processes, sharing reusable practices that increase productivity across engineering teams.
Technical Documentation: Document architectural decisions, implementation approaches, operational runbooks, agent workflows, and post-launch findings to support knowledge sharing and long-term maintainability.
Continuous Improvement: Identify opportunities to reduce development cycle times, improve engineering efficiency, and strengthen the relationship between technical delivery and measurable product impact.

Requirements

Professional Experience: Extensive professional experience in software engineering, with a proven track record of leading complex technical initiatives and delivering production-ready product solutions. Experience operating at a senior technical leadership or Staff Engineer level is essential.
AI-Native Development: Strong practical experience using AI coding agents such as Claude Code, Cursor, Codex, or equivalent tools as part of daily production development. Ability to direct AI agents through effective prompting, agent loops, evaluation frameworks, and review workflows.
AI-Assisted Engineering Quality: Experience building development workflows in which AI agents perform substantial implementation work while you remain responsible for technical direction, code quality, testing, and production readiness.
Technical Leadership: Demonstrated ability to make independent architectural and engineering decisions, solve complex technical problems, manage trade-offs, and take accountability for the success of delivered solutions.
Product Engineering Mindset: Strong understanding of product development principles, with the ability to translate business needs and customer problems into practical technical solutions that deliver measurable results.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

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