Job
Description
We are looking for a Full Stack AI Engineer with a deep understanding of creating intelligent AI agents and conversational systems. As a Full Stack AI Engineer, your primary responsibility will involve designing, developing, and deploying advanced AI agents using frameworks like LangChain, LangGraph, and other related technologies. You will also be expected to build multi-agent systems with intricate workflows, tool integration, and decision-making abilities. Additionally, creating conversational AI interfaces with memory, context management, and multi-turn interactions will be a crucial part of your role. Your expertise in full-stack development will be essential in developing responsive web applications using modern frontend frameworks such as React, Vue.js, or Angular. You will also work on building robust backend APIs and microservices using Python (FastAPI, Django) or Node.js, along with designing and implementing databases (SQL/NoSQL) for storing conversation history, user data, and agent knowledge. Real-time communication systems using technologies like WebSockets or Server-Sent Events will also fall under your purview, as well as deploying and maintaining applications on cloud platforms like AWS, GCP, or Azure. As a Full Stack AI Engineer, you will need to integrate AI agents with external APIs, databases, and third-party services. Implementing caching strategies and performance optimizations for AI workflows, building monitoring and logging systems for agent performance and user interactions, and ensuring the scalability, security, and reliability of AI-powered applications will be crucial aspects of your role. In terms of technical skills, you should have proficiency in AI/ML frameworks like LangChain, LangGraph, and agent orchestration patterns. Strong programming skills in Python, JavaScript/TypeScript for frontend development, experience with LLM integration, backend technologies like FastAPI, Django, Flask, or Node.js/Express, frontend frameworks like React, Vue.js, Angular, databases such as PostgreSQL, MongoDB, Vector databases, and cloud platforms like AWS, GCP, or Azure with containerization will be required. Moreover, your expertise should include building multi-step reasoning agents, knowledge of prompt engineering, chain-of-thought reasoning, and agent planning, understanding of vector embeddings, semantic search, RAG architectures, agent memory systems, conversation state management, RESTful APIs, microservices architecture, version control (Git), CI/CD pipelines, problem-solving, and debugging skills. Preferred qualifications include experience with additional agent frameworks, knowledge of fine-tuning and training custom models, familiarity with streaming responses and real-time AI interactions, understanding of AI safety, content filtering, responsible AI practices, background in natural language processing or machine learning research, experience with graph databases, knowledge graphs, MLOps, and AI model deployment pipelines. In this role, you will work on building intelligent chatbots and virtual assistants with complex reasoning capabilities, multi-agent systems for collaboration and task delegation, RAG-powered knowledge systems, AI-powered workflow automation tools, and conversational interfaces that integrate with business systems and databases.,
