Sde 3 (backend)

Pepper · Maharashtra, India

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
Type Script — strong typing across backend services and shared libraries
Deep experience with My SQL, Postgre SQL, 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, Rabbit MQ, Bull MQ 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 (Lang Chain, Lang Graph, Crew AI 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 — Open AI, 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