Mern Stack Developer

Role Overview

We are looking for a MERN Full Stack Developer with strong hands-on experience in building production-grade applications using MongoDB, Express.js, React.js, and Node.js, along with practical experience in integrating AI and GenAI capabilities into web applications. The candidate should be comfortable owning complex modules, making technical decisions, and building scalable applications across frontend, backend, database, and AI integrations.

Key Responsibilities

• Design and develop scalable applications using MongoDB, Express.js, React.js, and Node.js.
• Build reusable and performance-focused frontend components using React.js and TypeScript.
• Develop secure backend services and REST APIs using Node.js and Express.js.
• Design MongoDB schemas, indexes, aggregations, and optimized queries.
• Integrate LLMs and AI APIs into application workflows.
• Build AI-powered features using RAG, embeddings, vector search, tool calling, and agent-based workflows.
• Implement authentication, authorization, third-party API integrations, and external services.
• Handle application performance, security, error handling, logging, and production troubleshooting.
• Review code, define development standards, and mentor developers.
• Participate in solution design and architecture decisions for new features and modules.

Required Skills & Experience

• 3 to 5 years of full-stack development experience with strong hands-on expertise in MongoDB, Express.js, React.js, and Node.js.
• Strong proficiency in JavaScript and TypeScript.
• Strong experience with React.js, including Hooks, state management, component architecture, and performance optimization.
• Strong backend development experience using Node.js and Express.js.
• Experience designing and consuming REST APIs.
• Skilled in automating workflows using AI-assisted coding and multi-agent orchestration.
• Strong foundation in core web development principles and modern design systems.
• Strong knowledge of MongoDB data modelling, indexing, aggregation, and query optimization.
• Experience with JWT, OAuth, role-based access control, and API security.
• Hands-on experience integrating LLMs or GenAI APIs such as OpenAI, Anthropic, Gemini, Azure OpenAI, or equivalent.
• Experience with at least one AI application framework such as LangChain, LangGraph, LlamaIndex, or equivalent.
• Practical experience implementing RAG pipelines, embeddings, vector search, prompt workflows, and tool or function calling.
• Experience working with vector databases or vector search, such as Pinecone, Weaviate, Qdrant, pgvector, MongoDB Vector Search, or equivalent.
• Working knowledge of AI agents and agentic application workflows.
• Ability to handle production concerns for AI features, including response accuracy, latency, token usage, cost, security, and failure handling.
• Strong experience with Git, code reviews, debugging, unit testing, and integration testing.

Good to Have

• Experience with Next.js.
• Working knowledge of Python for AI services or integrations.
• Experience with Docker, CI/CD, and AWS, Azure, or GCP.
• Exposure to Redis, queues, microservices, and asynchronous processing.
• Familiarity with Model Context Protocol (MCP).
• Experience building AI assistants, copilots, document intelligence, workflow automation, or conversational applications