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