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