Agentic AI Engineer

Role Summary: We are looking for an AI Engineer specializing in Agentic AI systems and cloud-native deployment. This role focuses on building intelligent, autonomous systems using LLMs, RAG architectures, and emerging protocols like MCP, with an emphasis on scalable, production-ready implementations. Tech Stack: Python FastAPI, Flask LangChain, LlamaIndex AWS, Azure, GCP Docker, Kubernetes Key Responsibilities: Design and build Agentic AI systems capable of planning, reasoning, and tool usage Develop and optimize RAG (Retrieval-Augmented Generation) pipelines for enterprise use cases Implement and integrate Model Context Protocol (MCP) or similar frameworks for tool orchestration Build multi-agent workflows and autonomous decision-making systems Deploy AI applications on cloud platforms (AWS, Azure, GCP) with scalability and reliability Develop APIs and services to integrate LLM-powered features into products Work with vector databases and retrieval systems for efficient knowledge access Optimize latency, cost, and performance of LLM-based applications Implement observability, monitoring, and evaluation frameworks for AI systems Collaborate with product and engineering teams to deliver production-grade AI solutions Required Skills: Strong proficiency in Python and modern backend frameworks (FastAPI, Flask, etc.) Hands-on experience with LLM frameworks such as LangChain, LlamaIndex, or similar Strong understanding of RAG architectures, embeddings, chunking, and retrieval strategies Experience with vector databases (Pinecone, Weaviate, FAISS, Chroma, etc.) Experience building agentic workflows (tool use, memory, planning, orchestration) Familiarity with MCP (Model Context Protocol) or similar tool-interaction paradigms Experience deploying AI applications on cloud platforms (AWS/Azure/GCP) Strong knowledge of Docker, Kubernetes, and microservices architecture Experience designing and consuming REST APIs / async systems