Lead Product Engineer

Myntra; Bengaluru; On-site; INR 4,500,000 - 8,500,000; Full time

Job summary

Myntra Designs Private Limited in Bengaluru seeks a Lead Product Engineer to define the platform's technical direction and own architecture from ideation to production. You will lead development across backend, frontend, databases, and integrations, enabling configurable promotions and multi-brand campaigns.

The role emphasizes building modular, reusable platforms with AI-assisted workflows, mentoring a small team, and driving rapid product iteration while maintaining quality and security.

Qualifications

• 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.
• Experience designing REST APIs, database architectures, integrations, and scalable services.

Responsibilities

• 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.

Skills

Full-stack engineering

System architecture

Product mindset

Mentoring

Hands-on coding

Tools

Docker

CI/CD

GitHub Copilot

Claude AI

Codex

Cursor

Job description

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.