Senior Backend Developer (MERN Stack — AI-First)

Role: Senior Backend Developer (MERN Stack — AI-First) Experience: 5+ Years Location: Gurugram Role Summary As a Senior Backend Engineer, you will design and implement robust, scalable, high-performance backend services while working in an AI-first, agentic engineering culture. You will contribute to architectural decisions, solve complex technical problems, and mentor engineers across the stack, with a strong emphasis on Node.

js, Express.js, MongoDB, and backend architecture. Beyond building services, you will design and ship AI-powered product features (LLM-backed APIs, retrieval pipelines, agentic workflows) and use AI coding agents hands-on to accelerate your own delivery. Familiarity with Docker and CI/CD pipelines is a plus.

Key Responsibilities

Architect and implement backend services and REST APIs using Node.js and Express.js. Design scalable data models and work with MongoDB and NoSQL databases efficiently. Design and ship AI-powered product features — integrate LLM APIs, build retrieval-augmented (RAG) pipelines, and expose model-backed capabilities through clean, well-governed backend services.

Build and operate agentic workflows — tool-using agents, function/tool calling, orchestration, guardrails, evaluations, and human-in-the-loop checkpoints for production reliability. Work AI-first, hands-on — use AI coding agents (e.g. Claude Code, Cursor, Copilot) daily to design, write, review, and refactor code, and help set the team's standards for effective, safe AI-assisted development.

Solve complex problems related to performance, scalability, and data integrity. Ensure code quality through test coverage, peer reviews, and automated workflows — including evals and regression checks for AI/LLM features (accuracy, latency, cost, and safety). Manage background jobs, queues, and event-driven architecture, including asynchronous and long-running AI/agent jobs.

Collaborate with frontend developers, product managers, and DevOps for feature implementation. Lead the technical architecture of new features and services, including the AI/agent layer and its integration with core systems. Ensure application security and data protection protocols, including responsible-AI practices — prompt-injection defense, PII handling, data residency (DPDP), and cost/rate controls on model usage.

Optimize applications for performance, scalability, and maintainability. Mentor and guide junior engineers, ensuring best coding practices — and AI-assisted engineering practices — are followed. Participate in sprint planning, technical discussions, and release management.

Qualifications

B.Tech in Computer Science, Information Technology, or related field. 5+ years of hands-on experience with the MERN stack, especially in backend development. Proficiency in JavaScript and Node.js, with deep understanding of Express.js. Strong database knowledge, especially MongoDB, including schema design and optimization.

Hands-on experience building with LLMs — integrating model APIs (OpenAI, Anthropic, or similar), prompt design, structured outputs / tool calling, and shipping at least one AI feature to production or a serious prototype. Practical, day-to-day use of AI coding agents (Claude Code, Cursor, Copilot, or equivalent) as part of your development workflow.

Experience architecting scalable backend systems. Familiarity with Docker, containerization concepts, and CI/CD pipelines (e.g., GitHub Actions, Jenkins.) Understanding of REST APIs, microservices, and API documentation (Swagger/OpenAPI). Familiarity with Redis and message queues (RabbitMQ, Kafka) is a plus. Strong problem-solving and analytical skills.

Excellent written and verbal communication. Good to Have Experience with agent frameworks and orchestration (Lang. Chain, Lang. Graph, Llama. Index, or the Vercel AI SDK) and with the Model Context Protocol (MCP). Vector databases and retrieval (pgvector, Pinecone, Weaviate, MongoDB Atlas Vector Search) and embedding-based search.

LLM evaluation and observability — eval harnesses, tracing, and monitoring model cost, latency, and quality in production. Exposure to cloud platforms like AWS (EC2, S3, Lambda, Bedrock). Experience with monitoring/logging tools (ELK Stack, Prometheus, Grafana). Knowledge of GraphQL, WebSockets, or gRPC. Fine-tuning, RAG optimization, or self-hosted / open-weight model experience.

Contribution to open-source or public code repositories.