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Contentstack is an Agentic Experience Platform (AXP) that unifies structured content management, real-time customer data, omnichannel personalization, and autonomous AI orchestration. The company helps enterprises move beyond disconnected systems and manual workflows by connecting content, data, and AI to deliver faster, easier-to-manage, and more adaptive digital experiences.
You will join the Assets Platform team as an AI-Native Software Engineer, responsible for designing, building, and maintaining backend services and APIs that power Contentstack's asset management capabilities. Your work will span multiple technical domains:
**Assets Platform Development:** Design and build backend services for managing organizational structures, folders, files, and metadata with full CRUD operations. Implement and evolve access control and permission features, including asset sharing and visibility management. Build secure, performant asset delivery systems leveraging CDN, caching, and event-driven integrations. Enhance image editing capabilities (cropping, resizing, rotation, format conversion, compression, on-the-fly transformations) and video processing (trimming, transcoding, thumbnail generation, optimized streaming). Explore and integrate AI-powered media features such as smart cropping, background removal, auto-tagging, and automated metadata generation. Build asset usage tracking so users can see where assets are referenced before modification or deletion. Contribute to roadmap areas around security, collaboration, and sharing.
**AI-Native Engineering Practice:** Use AI tools and agents as your primary development partners for designing, writing, refactoring, testing, and reviewing code while maintaining full accountability for correctness, security, and maintainability. Break down product requirements into technical designs and implementation plans with AI assistance. Build and maintain reusable prompts, agent workflows, and project context for team-wide adoption of AI-driven SDLC practices. Leverage AI agents for multi-step work including large refactors, API changes, test generation, and bug triage. Measure and share the impact of AI-driven workflows on cycle time, quality, and defect rates. Follow company policies on data privacy and security when using AI tools, especially with customer data and secrets.
**Quality, Reliability, and Operations:** Own quality end-to-end with no separate QA hand-off. Write and maintain automated tests (unit, integration, end-to-end) with AI assistance, covering core flows such as upload, retrieval, editing, delivery, and permissions. Oversee AI-driven code reviews by setting standards, validating findings, and approving changes. Investigate and resolve production issues and customer-reported bugs using AI to accelerate log analysis and root-cause investigation. Participate in on-call rotations and incident response. Keep technical documentation current using AI to draft and maintain it.
**Collaboration:** Work closely with Product, Design, and partner engineering teams on cross-team features. Mentor other engineers on effective, responsible AI-assisted development.
Contentstack serves leading brands including Steve Madden, LG Electronics, Subaru of America, Dolce & Gabbana, ASICS, Decathlon, and Caesars Entertainment. The company is known for its customer-first culture and commitment to community through the Contentstack Cares program.
**Requirements:**
- 6+ years of professional software engineering experience building backend services and APIs
- Demonstrated, hands-on experience using AI coding tools and agents in daily work, with concrete examples of shipping production features through AI-driven workflows
- Strong skills in prompt design, providing effective context to AI tools, and critically evaluating AI output
- Experience with distributed systems, REST and gRPC APIs, event-driven architecture, and cloud infrastructure
- Solid understanding of access control, data security, and multi-tenant SaaS systems
- Strong testing discipline and debugging skills, with experience owning quality without a dedicated QA function
