SDE 3 (Backend)

SDE-3 — Backend Engineer

Location: Mumbai (preferred) / Remote, India

Employment Type: Full-time

Experience: 6–10 Years

About the Rol

eWe 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 Responsibiliti

• esDesign and own backend systems and services that are scalable, secure, and highly availab
• leLead technical architecture decisions — service decomposition, data modelling, API design, and system reliabili
• tyBuild and operate AI-native backend infrastructure — LLM orchestration layers, agentic pipelines, RAG systems, tool-use frameworks, and evaluation loo
• psDefine and enforce backend engineering standards, code quality practices, and security patter
• nsCollaborate with product, frontend, and data teams to deliver complex, cross-functional featur
• esOwn performance at scale — query optimisation, caching strategies, infrastructure bottlenec
• ksDrive incident response, root cause analysis, and reliability improvemen
• tsMentor SDE-1 and SDE-2 engineers, grow technical depth across the te

am

Must-Have Ski

• llsExpert-level Node.js backend development — services, APIs, event-driven architect
• ureTypeScript — strong typing across backend services and shared librar
• iesDeep experience with MySQL, PostgreSQL, and Redis — schema design, query optimisation, indexing, cach
• ingREST APIs and microservices architecture — design patterns, versioning, contract test
• ingSolid understanding of system design — distributed systems, consistency, fault tolerance, scalabil
• ityExperience with message queues and async processing (Kafka, RabbitMQ, BullMQ or equivale
• nt)CI/CD pipelines, containerisation (Docker/Kubernetes), and production deployment practi
• cesStrong testing discipline — unit, integration, contract, and load test
• ingAI-native thinking — fluency with LLMs, prompt engineering, and agentic system desi

gn.

Strongly Prefe

• rredHands-on experience building and operating agentic AI systems in production orchestration (LangChain, LangGraph, CrewAI or equivalent), tool use, memory, and evaluation framew
• orksExperience with RAG pipelines, vector databases (Pinecone, Weaviate, pgvector), embedding models, chunking and retrieval strate
• giesMulti-model LLM integration, OpenAI, Anthropic, Gemini, open-source models — with guardrails and fallback patt
• ernsExposure to data platform engineering or ML infrastructure is a p

lus.

What Success Looks

• LikeYou independently lead and deliver complex backend systems end t
• o endYour architecture decisions hold up at scale, performance, reliability, and maintainab
• ilityYou are the go-to person for production issues, system design reviews, and backend stan
• dardsYou are a multiplier for the team — engineers around you get better because o
• f youYou 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