{"id":3782897,"url":"https://alion.io/job/myntra-lead-product-engineer","title":"Lead Product Engineer","company":{"id":1431,"name":"Myntra","domain":"myntra.com","url":"https://alion.io/company/myntra","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Backend","role_family":"Backend","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Browser Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"Docker","optional":false},{"name":"LLM","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"Rest API","optional":false}],"status":"live","first_seen_at":"2026-11-05T01:35:45Z","employer_posted_date":null,"last_verified_at":"2026-11-05T01:35:45Z","board_verified":false,"closed_at":null,"days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"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.\n\nThe core responsibilities for the job include the following:\n\nEnd-to-End Product Development: \nOwn the technical development of the platform from architecture and technology selection to production deployment.\nDesign and develop backend services, APIs, frontend applications, databases, integrations, and infrastructure.\nBuild the administrative and configuration layers required to manage multiple promotions and programs.\nTranslate business requirements into scalable, maintainable product capabilities.\nTake ownership of technical decisions and production outcomes.\n\nBuild a Configurable, Reusable Platform: \nDesign the platform around reusable modules rather than client-specific implementations.\nIdentify common promotion patterns and convert them into configurable product capabilities.\nBuild flexible campaign engines, rules, workflows, and integration frameworks.\nEnsure clear separation between core product logic and client-specific configurations.\nFollow the principle: Configure repeatedly. Build once. Customize only by exception.\n\nAI-Native and Agentic Development: \nMake AI tools and coding agents an integral part of everyday engineering workflows.\nUse AI for requirement interpretation, solution design, code generation, API development, testing, debugging, and documentation.\nBuild agentic workflows for automated code reviews, regression testing, security checks, refactoring, and release validation.\nEstablish reusable prompts, engineering context, and AI-assisted development practices.\nContinuously improve engineering productivity, release frequency, and output per developer.\nTools and technologies may include Cursor, Claude, OpenAI Codex, GitHub Copilot, AI coding, and browser agents.\nWorkflow orchestration tools: Automated QA and testing tools, internally developed engineering agents.\nThe expectation is to use AI beyond code autocomplete and integrate it meaningfully across the software development lifecycle.\n\nArchitecture and Technical Ownership: \nDefine the overall architecture, technology stack, data models, and engineering standards.\nBuild modular, API-first, scalable, and maintainable systems.\nDesign event-driven workflows and distributed services wherever appropriate.\nEnsure support for multiple brands, campaigns, promotion formats, and integrations.\nMake pragmatic architecture decisions that balance scalability, simplicity, speed, and maintainability.\nOwn technical trade-offs and ensure the platform remains manageable for a lean engineering team.\n\nRapid Product Development and Execution: \nDrive short development cycles, rapid prototyping, and continuous product iteration.\nConvert ideas and business requirements into working prototypes and production-ready capabilities.\nEnable the team to move from idea to prototype and production in weeks wherever practical.\nUse AI-assisted development to accelerate implementation without compromising engineering quality.\nEstablish a culture of experimentation, ownership, and fast execution.\n\nTechnical Team Leadership: \nLead and mentor a small engineering team of approximately 5-6 engineers.\nParticipate in hiring, technical mentoring, sprint planning, and prioritization.\nEstablish engineering practices, coding standards, and quality benchmarks.\nDrive architecture discussions, code reviews, and technical decision-making.\nImprove team productivity through effective adoption of AI tools and agentic workflows.\nRemain actively involved in coding, prototyping, debugging, and reviewing critical components.\n\nProduction Engineering and Quality: \nEstablish automated testing, CI/CD pipelines, and release management practices.\nEnsure production readiness, application security, performance, and reliability.\nImplement monitoring, logging, observability, and incident-management workflows.\nEstablish deployment, rollback, backup, and recovery mechanisms.\nIntroduce AI-assisted testing, code validation, debugging, and incident analysis.\nEnsure AI-generated code meets the same quality and security standards as manually developed code.\n\nRequirements: \n8-15 years of experience in software engineering, product development, or full-stack engineering.\nProven experience building and launching products or platforms from scratch.\nStrong hands-on full-stack engineering and system architecture capabilities.\nStrong backend development experience with modern programming languages and frameworks.\nExperience designing REST APIs, database architectures, integrations, and scalable services.\nStrong understanding of relational and NoSQL databases, caching, and asynchronous/event-driven systems.\nExperience with cloud infrastructure, application deployment, Docker, and CI/CD pipelines.\nExperience building modular, reusable, and configurable software platforms.\nHands-on experience using AI coding tools such as Cursor, Claude, Codex, GitHub Copilot, or similar.\nPractical experience integrating LLMs, AI agents, or AI-powered workflows into software development or products.\nExperience with automated testing, code quality, monitoring, and production operations.\nExperience leading or mentoring small engineering teams while remaining technically hands-on.\nStrong product mindset with the ability to translate business requirements into practical technology solutions.\nSkills\nAI, APIs, Architecture, CI / CD, Claude, Codex, Copilot, Docker, Full Stack, Full-Stack, LLM, REST","description_format":"text","description_chars":5522,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Marketplaces","Apparel & Fashion Brands"],"lifecycle":[{"event":"open","at":"2026-11-05T01:35:45Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring 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