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