About Razorpay
Razorpay is one of India’s leading full-stack financial technology companies, empowering businesses to manage and grow their money. Founded in 2014, Razorpay has evolved into a fintech powerhouse, driving India’s digital payment revolution by providing a smarter, scalable stack that extends beyond transactions to facilitate business growth. We are embedding intelligence across our stack, from AI-native agentic payments and AI-assisted fraud detection to real-time risk intelligence, automated reconciliation, smart payouts, and predictive financial insights. This is achieved in close collaboration with ecosystem partners, pioneering industry-first solutions that are shaping the next era of fintech.
With operations in India, Singapore, and Malaysia, our products encompass seamless checkouts, payroll automation, and more, supporting a fintech ecosystem that redefines money movement across Asia. We serve a diverse range of clients, from early-stage startups to India's largest enterprises, enabling them to accept, process, and disburse payments at scale while expanding into new, efficient ways of managing money. Razorpay processes over $180 billion in annualized transactions and is backed by global investors including GIC, Peak XV Partners, Tiger Global, and Salesforce Ventures.
At Razorpay, our culture prioritizes ownership, continuous learning, and transparency. We believe in customer-first approaches, autonomy, agility with integrity, and challenging the status quo. Join our 3000+ strong team to build the financial infrastructure of the future.
About the Role
This role is within the Central AI Initiatives team, reporting to the Marketing Team. You will be responsible for the entire AI solution lifecycle: identifying high-impact business problems, conceiving AI-powered solutions, building production systems, deploying them, and owning the outcomes. The nature of the problems will shift every few weeks based on company priorities, requiring rapid adaptation and execution. This is a builder role that demands expertise in problem decomposition, system design, production deployment, and cross-organizational influence, integrating aspects of product management, data science, and engineering.
Responsibilities
- Decompose business problems: Break down complex business briefs into data, models, integrations, automations, and human touchpoints, making informed decisions on what to build, buy, or defer.
- Design and build full systems: Architect, develop backend services, create data pipelines, orchestrate LLMs, and build frontends. Write production-ready code that delivers tangible results.
- Ship to production and own outcomes: Manage phased rollouts, set alert thresholds, monitor business metrics, troubleshoot issues, and understand root causes when targets are not met.
- Switch altitude: Seamlessly transition between technical depth (debugging model internals) and strategic leadership context (presenting roadmaps, reviewing legal terms for AI partnerships).
- Multiply AI capability: Empower teams across Sales, Marketing, Risk, and Engineering to identify and execute AI opportunities through frameworks, reference tools, and workshops.
Requirements
You have been actively using AI tools for over a year, with Claude Code or Cursor as your default environment. You are proficient in orchestrating agents, building with skills and MCPs, and applying AI assistance as muscle memory. You are a polyglot, quickly adapting to new technology stacks, and you thrive in ambiguity, defaulting to action.
Key Qualifications
- Founder mindset: You have initiated and brought a company, product, or team from conception to live deployment with your own hands, demonstrating self-starter capabilities.
- Experience: 3–5 years in software development, with at least 1–2 years specifically building AI/ML-powered products in production environments. Depth of experience is prioritized over mere years.
- Full-stack capability: Hands-on experience with backend services, data pipelines (Kafka, Airflow), LLM orchestration (OpenAI, Anthropic, Gemini), frontends (React), and deployment infrastructure (GCP, Docker). You are capable of unblocking yourself.
- LLM & agent depth: Practical experience with multi-agent systems, RAG, prompt engineering, and production AI pipelines, beyond simple API wrappers.
- Product instinct: You ask “why” before “how,” can scope business problems into shippable prototypes, and articulate business value to non-technical stakeholders.
- Speed & autonomy: Ability to take a problem from discovery to demo in under two weeks, moving faster than processes and creating structure only when beneficial.
- Influence without authority: You can align stakeholders across Sales, Risk, Engineering, and Legal through clarity, credibility, and speed, even without direct reporting lines.
Not a Good Fit If
- You require detailed specifications.
- You prefer deep specialization over broad capabilities.
- You are uncomfortable with ambiguity.
- You prioritize code elegance over customer outcomes.
- You disengage after design or shipping.
- You use “not my job” as a valid response.
- You need a stable roadmap for optimal performance.
- You thrive within a rigid 2-week sprint cycle.
Recent Projects (Examples)
- Browser-based AI Agent: An autonomous web navigation agent that crawls merchant websites, detects payment gateways, and places test orders. Used by Risk Ops for mystery shopping and fraud detection, involving headless browser technology, LLM-driven navigation, and real-time Slack alerting.
- Ops Copilot Platform: A full-stack employee activity intelligence system, utilizing Rust-based macOS agents for real-time monitoring.