Kraftshala
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Delhi · On-site · 2-5 Yrs · Sep 25
Education Support & Training Services · 51 to 200 Employees · New Delhi, India
Kraftshala is on a mission to help a million students launch their careers in marketing, sales, and more. We’ve already helped thousands of students launch their marketing careers in companies like Performics, Sokrati, GroupM, Nykaa, Tata Cliq, Coursera and are looking to add hundreds of thousands more in the coming decade. Kraftshala is backed by a number of eminent investors including ex-unicorn startup founders and CXOs of global giants. At Kraftshala, we believe that higher education in India and across the globe lacks accountability to the students they are meant to serve, in large part because the people working in the system aren’t provided meaningful incentives to improve career outcomes for students. It’s also what keeps the best industry practitioners from getting involved in training, and in turn, perpetuating a culture of mediocrity. We are trying to change that around by building pay-after-placement programs that ensure our students are trained by industry experts who are passionate about injecting care in the higher education ecosystem. Funding: Kraftshala is backed by a number of eminent investors, including: - Phanindra Sama and Sudhakar Pasupunuri, co-founders at Redbus and early investors in unicorns like Unacademy - Amit Kumar Agarwal, Saurabh Garg and Akhil Gupta, co-founders at Nobroker.com (the world’s largest NoBrokerage Property Site and India’s first realty startup unicorn) - Sujayathi Ali, Co-founder at ShopUp and Voonik - Doreswamy Nandkishore (Former Fortune 100 CEO, Head of Asia, Oceania, Africa for Nestlé S.A. and Professor, ISB) - Pallav Jain and Sarfaraz Khimani (co-CEOs of Performics, India's largest SEO/ performance marketing agencies) What do we value (the Kraftshala Kode): Like all good companies, we value integrity, excellence, respect, inclusion, and collaboration. Our team’s distinctiveness comes from the following: - We seek a user-centric, value-additive mindset. People who start with the user and work backward, and take every opportunity to add value to their customers/ partners fit right in. - We believe speed matters enormously in business. We value people who have a bias for action and are willing to take calculated risks in the face of uncertainty. - We value problem-solving skills. We look at problems objectively, solve for root causes, make decisions for long-term good instead of short-term gain, and don’t let processes get in the way of value addition. - We look for learning agility. We are a team of learners who like to read, talk to experts, and seek critical feedback to be better today than we were yesterday. - We respect ambition & courage to take on large goals. Self-starters who demonstrate high ownership strengthen the backbone of our team. - We expect consistency and reliability. Predictability just makes everyone’s life a lot smoother. - We like people who are real and open in communication, while still being empathetic. - We are practical optimists. Our team strongly believes in a better future & we enjoy playing our part to make that future a reality.
Hiring: Engineering
What employees say
+ 1) The learning opportunities are excellent, and senior management is supportive. 2) The company gives you time to adapt and offers various opportunities to learn and pick up new
− Since the company is growing, there are frequent changes. you sometimes need to adapt quickly, or else you might get left behind.
More reviews on the company profile.
Interview experience · Senior Digital Marketing Associate
First round was assignment round where we had to audit the entire website for SEO and recommend improvements Second round was technical interview Third round was culture fit interview with the founder
Very_easy difficulty · India · 19 reports
More interview insightsJob description
Overview
Who we're looking for: A strong full-stack engineer who deeply knows their stack and can also build AI products end-to-end.
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.
- 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.
- 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. .
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Job application FAQ
Last updated 4 October 2026
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