AI Full Stack Engineer

About the Role

We are looking for a AI Full Stack Engineer to design, develop, and deploy production-grade AI applications, autonomous agents, and intelligent full-stack solutions.

The ideal candidate is more than a traditional full-stack developer or prompt engineer. We are looking for a strong problem solver and technology owner who can build agentic systems, integrate tools using Model Context Protocol (MCP), develop scalable Python and React applications, and work with both NoSQL and relational databases.

You will be responsible for taking AI solutions from concept and architecture through development, testing, deployment, optimization, and production support.

Key Responsibilities

1. AI Agents & Agentic Workflows

• Design, develop, and deploy production-grade AI agents and multi-agent workflows.
• Build agentic systems using LangGraph, CrewAI, AutoGen, or custom agent frameworks.
• Implement planning, reasoning, tool invocation, routing, memory, and state management.
• Develop reliable workflows with deterministic fallbacks and structured outputs.
• Diagnose hallucinations, context limitations, execution loops, rate limiting, and other AI-system issues.
• Establish evaluation and monitoring mechanisms for agent performance and reliability.

2. Model Context Protocol (MCP)

• Design and implement MCP clients and servers.
• Develop secure and reusable MCP tools for connecting LLMs with APIs, enterprise applications, databases, and other tools.
• Define and maintain MCP tool schemas, inputs, outputs, authentication, and permissions.
• Troubleshoot tool invocation, context propagation, schema validation, and integration issues.
• Establish standards and best practices for MCP-based enterprise integrations.

3. Python Backend Engineering

• Develop scalable backend services using Python, FastAPI, or Flask.
• Build asynchronous APIs and services supporting AI agents and LLM workflows.
• Implement REST APIs, streaming APIs, SSE/WebSocket-based communication, and integrations.
• Integrate LLMs, external APIs, enterprise systems, and AI tools.
• Implement authentication, authorization, API security, logging, monitoring, and error handling.
• Optimize backend performance, reliability, and scalability.

4. React Full-Stack Development

• Build modern, responsive applications using React with JavaScript/TypeScript.
• Develop reusable and modular UI components.
• Implement real-time AI experiences including token streaming and agent execution status.
• Build interfaces for conversations, agent workflows, tool execution, results, and system monitoring.
• Manage React state, component lifecycle, API integration, and asynchronous workflows.

5. Database & Data Architecture

• Design and maintain data models using MongoDB and relational SQL databases.
• Work with PostgreSQL, MySQL, or similar relational databases.
• Store conversation history, agent state, checkpoints, audit trails, evaluation results, and telemetry.
• Design indexes and optimize queries for high-performance AI workloads.
• Perform database profiling, schema optimization, and latency reduction.

6. RAG & AI Application Development

• Build and integrate Retrieval-Augmented Generation (RAG) pipelines.
• Work with vector embeddings and vector databases.
• Develop document/data ingestion, chunking, retrieval, ranking, and context-generation workflows.
• Implement mechanisms to improve response accuracy and reduce hallucinations.

7. AI-Assisted Development & Automation

• Leverage modern AI coding assistants and generative AI developer tools to accelerate development.
• Use AI tools for prototyping, code generation, refactoring, testing, debugging, and documentation.
• Identify repetitive development and operational processes and automate them.
• Continuously evaluate emerging AI frameworks, models, protocols, and engineering tools.

Required Qualifications

• 3 7 years of professional software engineering experience building production applications.
• Strong hands-on experience with Python.
• Production experience with FastAPI and/or Flask.
• Strong understanding of asynchronous programming and API development.
• Hands-on experience building AI agents or agentic workflows.
• Practical experience with Model Context Protocol (MCP), including MCP clients, servers, or tool integrations.
• Strong React development experience using JavaScript and/or TypeScript.
• Experience with real-time streaming technologies such as SSE or WebSockets.
• Hands-on experience with both MongoDB and SQL databases.
• Strong understanding of REST APIs, JSON, authentication, and API security.
• Experience working with LLMs and integrating AI/ML APIs into applications.
• Strong debugging, analytical, and problem-solving skills.
• Ability to take ownership of a problem from investigation through production deployment.