SDE 3 (Backend)

Job Description

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

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• Design and own backend systems and services that are scalable, secure, and highly available
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• Lead technical architecture decisions — service decomposition, data modelling, API design, and system reliability
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• Build and operate AI-native backend infrastructure — LLM orchestration layers, agentic pipelines, RAG systems, tool-use frameworks, and evaluation loops
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• Define and enforce backend engineering standards, code quality practices, and security patterns
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• Collaborate with product, frontend, and data teams to deliver complex, cross-functional features
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• Own performance at scale — query optimisation, caching strategies, infrastructure bottlenecks
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• Drive incident response, root cause analysis, and reliability improvements
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• Mentor SDE-1 and SDE-2 engineers, grow technical depth across the team
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Must-Have Skills

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• Expert-level Node.js backend development — services, APIs, event-driven architecture
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• TypeScript — strong typing across backend services and shared libraries
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• Deep experience with MySQL, PostgreSQL, and Redis — schema design, query optimisation, indexing, caching
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• REST APIs and microservices architecture — design patterns, versioning, contract testing
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• Solid understanding of system design — distributed systems, consistency, fault tolerance, scalability
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• Experience with message queues and async processing (Kafka, RabbitMQ, BullMQ or equivalent)
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• CI/CD pipelines, containerisation (Docker/Kubernetes), and production deployment practices
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• Strong testing discipline — unit, integration, contract, and load testing
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• AI-native thinking — fluency with LLMs, prompt engineering, and agentic system design
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Strongly Preferred

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• Hands-on experience building and operating agentic AI systems in production — orchestration (LangChain, LangGraph, CrewAI or equivalent), tool use, memory, and evaluation frameworks
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• Experience with RAG pipelines — vector databases (Pinecone, Weaviate, pgvector), embedding models, chunking and retrieval strategies
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• Multi-model LLM integration — OpenAI, Anthropic, Gemini, open-source models — with guardrails and fallback patterns
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• Exposure to data platform engineering or ML infrastructure is a plus
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What Success Looks Like

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• You independently lead and deliver complex backend systems end to end
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• Your architecture decisions hold up at scale — performance, reliability, and maintainability
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• You are the go-to person for production issues, system design reviews, and backend standards
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• You are a multiplier for the team — engineers around you get better because of you
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• 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
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