SDE 3 (Backend)

SDE-3 — Backend Engineer Location: Mumbai (preferred) / Remote, India Employment Type: Full-time Experience: 6–10 Years About the Role We are looking for a Senior Backend Engineer (SDE-3) to own the design, scalability, and reliability of Pepper's backend systems. You will operate at the intersection of engineering depth and product thinking — leading architecture decisions, building AI-native backend infrastructure, and mentoring a growing engineering team. This role is for someone who thinks in systems, not just services, and who treats agentic AI patterns as a natural part of modern backend design. Key Responsibilities Design and own backend systems and services that are scalable, secure, and highly available Lead technical architecture decisions — service decomposition, data modelling, API design, and system reliability Build and operate AI-native backend infrastructure — LLM orchestration layers, agentic pipelines, RAG systems, tool-use frameworks, and evaluation loops Define and enforce backend engineering standards, code quality practices, and security patterns Collaborate with product, frontend, and data teams to deliver complex, cross-functional features Own performance at scale — query optimisation, caching strategies, infrastructure bottlenecks Drive incident response, root cause analysis, and reliability improvements Mentor SDE-1 and SDE-2 engineers, grow technical depth across the team Must-Have Skills Expert-level Node.js backend development — services, APIs, event-driven architecture TypeScript — strong typing across backend services and shared libraries Deep experience with MySQL, PostgreSQL, and Redis — schema design, query optimisation, indexing, caching REST APIs and microservices architecture — design patterns, versioning, contract testing Solid understanding of system design — distributed systems, consistency, fault tolerance, scalability Experience with message queues and async processing (Kafka, RabbitMQ, BullMQ or equivalent) CI/CD pipelines, containerisation (Docker/Kubernetes), and production deployment practices Strong testing discipline — unit, integration, contract, and load testing AI-native thinking — fluency with LLMs, prompt engineering, and agentic system design Strongly Preferred Hands-on experience building and operating agentic AI systems in production — orchestration (LangChain, LangGraph, CrewAI or equivalent), tool use, memory, and evaluation frameworks Experience with RAG pipelines — vector databases (Pinecone, Weaviate, pgvector), embedding models, chunking and retrieval strategies Multi-model LLM integration — OpenAI, Anthropic, Gemini, open-source models — with guardrails and fallback patterns Exposure to data platform engineering or ML infrastructure is a plus What Success Looks Like You independently lead and deliver complex backend systems end to end Your architecture decisions hold up at scale — performance, reliability, and maintainability You are the go-to person for production issues, system design reviews, and backend standards You are a multiplier for the team — engineers around you get better because of you You bring AI into the backend not as an integration, but as a design instinct — agentic patterns, LLM tooling, and intelligent automation are part of how you think