Lead Product Engineer

The product architecture will evolve as the platform takes shape, and the Lead Product Engineer will play a critical role in defining its technical direction.

The core responsibilities for the job include the following:

End-to-End Product Development:

• Own the technical development of the platform from architecture and technology selection to production deployment.
• Design and develop backend services, APIs, frontend applications, databases, integrations, and infrastructure.
• Build the administrative and configuration layers required to manage multiple promotions and programs.
• Translate business requirements into scalable, maintainable product capabilities.
• Take ownership of technical decisions and production outcomes.

Build a Configurable, Reusable Platform:

• Design the platform around reusable modules rather than client-specific implementations.
• Identify common promotion patterns and convert them into configurable product capabilities.
• Build flexible campaign engines, rules, workflows, and integration frameworks.
• Ensure clear separation between core product logic and client-specific configurations.
• Follow the principle: Configure repeatedly. Build once. Customize only by exception.

AI-Native and Agentic Development:

• Make AI tools and coding agents an integral part of everyday engineering workflows.
• Use AI for requirement interpretation, solution design, code generation, API development, testing, debugging, and documentation.
• Build agentic workflows for automated code reviews, regression testing, security checks, refactoring, and release validation.
• Establish reusable prompts, engineering context, and AI-assisted development practices.
• Continuously improve engineering productivity, release frequency, and output per developer.
• Tools and technologies may include Cursor, Claude, OpenAI Codex, GitHub Copilot, AI coding, and browser agents.
• Workflow orchestration tools: Automated QA and testing tools, internally developed engineering agents.
• The expectation is to use AI beyond code autocomplete and integrate it meaningfully across the software development lifecycle.

Architecture and Technical Ownership:

• Define the overall architecture, technology stack, data models, and engineering standards.
• Build modular, API-first, scalable, and maintainable systems.
• Design event-driven workflows and distributed services wherever appropriate.
• Ensure support for multiple brands, campaigns, promotion formats, and integrations.
• Make pragmatic architecture decisions that balance scalability, simplicity, speed, and maintainability.
• Own technical trade-offs and ensure the platform remains manageable for a lean engineering team.

Rapid Product Development and Execution:

• Drive short development cycles, rapid prototyping, and continuous product iteration.
• Convert ideas and business requirements into working prototypes and production-ready capabilities.
• Enable the team to move from idea to prototype and production in weeks wherever practical.
• Use AI-assisted development to accelerate implementation without compromising engineering quality.
• Establish a culture of experimentation, ownership, and fast execution.

Technical Team Leadership:

• Lead and mentor a small engineering team of approximately 5-6 engineers.
• Participate in hiring, technical mentoring, sprint planning, and prioritization.
• Establish engineering practices, coding standards, and quality benchmarks.
• Drive architecture discussions, code reviews, and technical decision-making.
• Improve team productivity through effective adoption of AI tools and agentic workflows.
• Remain actively involved in coding, prototyping, debugging, and reviewing critical components.

Production Engineering and Quality:

• Establish automated testing, CI/CD pipelines, and release management practices.
• Ensure production readiness, application security, performance, and reliability.
• Implement monitoring, logging, observability, and incident-management workflows.
• Establish deployment, rollback, backup, and recovery mechanisms.
• Introduce AI-assisted testing, code validation, debugging, and incident analysis.
• Ensure AI-generated code meets the same quality and security standards as manually developed code.

Requirements:

• 8-15 years of experience in software engineering, product development, or full-stack engineering.
• Proven experience building and launching products or platforms from scratch.
• Strong hands-on full-stack engineering and system architecture capabilities.
• Strong backend development experience with modern programming languages and frameworks.
• Experience designing REST APIs, database architectures, integrations, and scalable services.
• Strong understanding of relational and NoSQL databases, caching, and asynchronous/event-driven systems.
• Experience with cloud infrastructure, application deployment, Docker, and CI/CD pipelines.
• Experience building modular, reusable, and configurable software platforms.
• Hands-on experience using AI coding tools such as Cursor, Claude, Codex, GitHub Copilot, or similar.
• Practical experience integrating LLMs, AI agents, or AI-powered workflows into software development or products.
• Experience with automated testing, code quality, monitoring, and production operations.
• Experience leading or mentoring small engineering teams while remaining technically hands-on.
• Strong product mindset with the ability to translate business requirements into practical technology solutions.