Job Summary

Who we're looking for: A strong full-stack engineer who deeply knows their stack and can also build AI products end-to-end. You write clean, performant Node.js and TypeScript, model data well in SQL, build rapid search with Elasticsearch, work comfortably on AWS, and ship polished React and Gatsby frontends. You're backend-heavy and genuinely hands-on, but comfortable across the full stack. You can take an AI product from idea to production using LLMs, AI agents, APIs, and production-grade infrastructure, and you take real ownership of what you build. What the role is about: You'll design and build the systems behind our web experience - APIs, data models, search, infrastructure, and AI-powered products and workflows. You'll work across the full product lifecycle, from architecture and development to deployment and iteration. Strong engineering craft, AI product-building ability, and product sense are all core to this role.

Why you should apply: If you love building solid systems and turning AI capabilities into real products, this role gives you deep technical ownership and the opportunity to work on products that directly impact thousands of learners. You'll get to work across traditional software engineering and hands-on AI product development, with the freedom to build, ship, and iterate rather than simply consume AI tools.

Responsibilities

- Build and ship backend services and APIs: Design and build scalable backend systems in Node.js and TypeScript. Metrics to measure: API performance, reliability, delivery timelines, and post-release defects.
- Build AI-powered products end-to-end: Build production-ready AI products using LLMs, AI agents, APIs, and external systems - from architecture and implementation through production deployment and iteration. Metrics to measure: time to production, reliability, adoption, task completion, latency, and cost efficiency.
- Build and optimise data and search systems: Design performant SQL data models and Elasticsearch-powered search and discovery experiences. Metrics to measure:



query/search performance, data integrity, relevance, and indexing reliability.
- Own cloud infrastructure and deployment: Deploy, scale, monitor, and optimise backend and AI systems on AWS. Metrics to measure: uptime, incident frequency/recovery, deployment reliability, and infrastructure/AI costs.
- Build user-facing product experiences: Develop polished React/Gatsby experiences on top of the systems you own. Metrics to measure: feature adoption, page performance, and post-release defects.
- Own technical architecture and projects end-to-end: Make sound frontend, backend, and AI architecture decisions and independently take projects from problem definition through production. Metrics to measure: on-time delivery, system reliability and scalability, technical debt, and production performance.

Top Grading

- Problem Solving: An A-player breaks complex engineering and AI problems into elegant solutions independently, whereas a B-player needs frequent direction.
- Code Quality Craft: An A-player consistently writes clean, well-tested, maintainable code and builds reliable production systems, whereas a B-player prioritises getting something working over long-term quality.
- Ownership Accountability: An A-player takes full ownership of product outcomes from idea to production - including what happens after deployment - whereas a B-player limits responsibility to assigned tickets.
- AI Product Engineering: An A-player can independently turn an AI product idea into a reliable production system using LLMs, agents, APIs, and application engineering, whereas a B-player has primarily experimented with AI tools without being able to build complete products.
- Product Thinking:



An A-player deeply understands user needs and identifies where technology can genuinely improve the product, whereas a B-player implements requirements without enough consideration of the underlying user problem.

Must Haves

- 2-3 years of experience building web applications with Node.js and TypeScript on the backend, and React on the frontend. We are not too fussed about the number of years - experience is simply a proxy for capability, which is what we really care about.
- Hands-on experience building AI products end-to-end using LLMs, AI agents, and APIs, from problem definition and architecture through implementation, production deployment, and iteration.
- Strong proficiency in JavaScript/TypeScript fundamentals, including ES6+ features, asynchronous programming, and modular architecture.
- Solid experience designing and consuming RESTful APIs, and building backend services that are efficient, secure, well-documented, and clean.
- Strong command of SQL and relational database concepts - schema design, indexing, and writing performant queries.
- Hands-on experience with Elasticsearch for indexing, querying, and relevance tuning.
- Working knowledge of AWS for deploying, scaling, and monitoring services.
- Experience building frontends in React, with familiarity with Gatsby, and an eye for product polish and detail.

Good to Have

- Familiarity with GraphQL alongside REST.
- Experience with infrastructure-as-code and CI/CD pipelines on AWS.
- Exposure to caching, queuing, or event-driven architectures.
- Familiarity with Netlify, WordPress, or other JAMstack tooling.
- Contributions to the open-source ecosystem.
- Comfort with observability tooling such as logging, metrics, and tracing.

Disclaimer: This job posting and location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Sr Software Developer - Full Stack & AI Product Engineering (New Delhi)
🏢 Kraftshala
📍 New Delhi