Senior Software Developer - Full Stack And AI Product Engineering

Kraftshala; India; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Kraftshala in Delhi is seeking a senior software developer (backend-heavy with AI product engineering) who can own the design and delivery of scalable web applications. You will work across APIs, data models, search, and AI-powered workflows, writing clean Node.js/TypeScript, modeling data in SQL, and building fast frontends with React and Gatsby.

This role rewards deep technical ownership, end-to-end product thinking, and hands-on AI product development using LLMs, agents, and production-grade

Qualifications

• 2-3 years of experience building web apps with Node.js/TypeScript backend and React frontend.
• Hands-on AI product development using LLMs, agents, and APIs.
• Strong JavaScript/TypeScript fundamentals (ES6+, async, modular).
• RESTful APIs design and backend service development.
• Strong SQL skills: schema design, indexing, performant queries.
• Experience with Elasticsearch for indexing and relevance tuning.
• Experience deploying and monitoring on AWS.

Responsibilities

• Build and ship backend services and APIs with Node.js and TypeScript.
• Develop AI-powered products end-to-end using LLMs and AI agents.
• Design data models and Elasticsearch-powered search experiences.
• Own cloud infrastructure on AWS including deployment and monitoring.
• Build user-facing React/Gatsby interfaces on top of systems.

Skills

Node.js

TypeScript

React

SQL

Elasticsearch

AWS

Gatsby

API design

REST APIs

Frontend development

Tools

GraphQL

CI/CD pipelines

AWS IaC

Netlify

Job description

Job Description:

The Short Version

Who were 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 fast search with Elasticsearch, work comfortably on AWS, and ship polished React and Gatsby frontends. Youre 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:

Youll design and build the systems behind our web experience - APIs, data models, search, infrastructure, and AI-powered products and workflows. Youll 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. Youll 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.

About us:

Kraftshala is Indias largest higher-education institution in terms of marketing placements, with a 94% placement rate, and we are gunning to become the worlds largest career launchpad, across domains. We’re backed by a number of eminent investors including ex-unicorn startup founders and CXOs of global giants like RedBus, NoBroker, Nestlé, and Performics.

The Detailed Version

As a Senior Software Developer (Backend-Heavy & AI Product Engineering) at Kraftshala, youll work closely with product, design, and other engineers to build fast, reliable, scalable web applications and AI-powered products.

This role suits someone with strong engineering fundamentals and depth in their tools, combined with hands-on AI product-building ability, product thinking, attention to detail, and the ability to take ownership in a fast-moving startup.

Responsibilities & Metrics

• 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 Haves

• 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.

Selection Process

1. Setting Expectations: A call with the HR team to understand your profile and share the details of the selection process.
2. Technical Assessment: An assessment designed to evaluate your fit for the role, with a focus on backend problem-solving and hands-on AI product building.
3. Technical Interview: A conversation with our Tech Lead covering the core competencies needed for the role, including backend engineering, system design, product thinking, and AI product development.
4. Culture Fit Conversation: A conversation with our CEO to ensure there is a fit with the Kraftshala Kode.
5. Extending an Offer: If all goes well, well extend an offer with the relevant details.

Location

Delhi

Know more about Kraftshala’s philosophy, culture, and investors here.