Backend engineer focused on designing and shipping production systems — REST and event-driven APIs, service-to-service auth, and the data layer underneath them. Comfortable owning a feature from schema to deployment. Recent work sits at the intersection of backend infrastructure and applied AI: retrieval pipelines, agent tooling, and context-aware services built on LangChain, LangGraph, and MCP. I optimize for systems that are boring to operate — predictable, observable, and easy to roll back.
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Correctness before cleverness Code is read far more than it is written. I favor explicit types, small functions, and interfaces that fail loudly in development rather than silently in production. Design for the failure case Timeouts, retries, idempotency, and backpressure are not edge cases — they are the actual system. I design the failure path first, then the happy path. |
Scale is a budget, not a feature Premature horizontal scaling costs more than it saves. I profile before I shard, and I reach for caching, indexing, and query design before infrastructure. Developer experience compounds Fast local setup, typed contracts, and CI that catches regressions early are what let a team move quickly six months in, not just in week one. |
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Backend Engineering REST and RPC API design, authentication and authorization (JWT, OAuth2), service architecture, request validation, rate limiting, and background job processing with Node.js, Fastify, and Express. |
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AI Engineering RAG pipelines, agentic workflows with LangChain and LangGraph, Model Context Protocol integrations, embedding generation, and retrieval over Qdrant and PGVector. |
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Cloud & Systems Design Containerized deployments with Docker, CI/CD via GitHub Actions, AWS-hosted services, database schema design across PostgreSQL and MongoDB, Redis for caching and queues, and microservice decomposition. |
Languages
Backend
Frontend
Databases
Cloud & DevOps
AI & Vector Search
Tools
- Building production-grade MCP servers to expose internal tools and data sources to LLM agents
- Deepening system design fundamentals — consistency models, partitioning strategies, and queue-based architectures
- Extending a Fastify service template with built-in observability, structured logging, and typed request contracts
- Working through advanced graph and dynamic programming problems on LeetCode
Consistent practice in data structures, algorithms, and graph problems — with an emphasis on reasoning through time and space complexity before writing code.
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Portfolio rajesh-kayal-portfolio.vercel.app |
LinkedIn in/rajesh110 |
GitHub @rajesh-kayal-dev |
