About the Role We are looking for an Applied AI Engineer to design, build, and deploy intelligent AI solutions that solve real business challenges. In this role, you'll work with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), GraphRAG, AI agents, and knowledge graphs to create scalable, production-ready AI applications. This is an AI-first engineering role , where leveraging AI development tools such as ChatGPT, Cursor, GitHub Copilot, Claude, or Windsurf is an essential part of the development workflow. Key Responsibilities Design, develop, and deploy AI-powered applications using LLMs. Build and optimize RAG and GraphRAG pipelines. Develop AI agents and multi-agent workflows for business automation. Design and manage knowledge graphs using GraphDB technologies. Integrate enterprise data sources with AI applications. Engineer prompts, workflows, and agent orchestration for reliable AI systems. Evaluate and optimize AI models for quality, latency, scalability, and cost. Build production-ready APIs and AI services. Collaborate with cross-functional teams to deliver AI-driven products. Stay up to date with the latest AI models, frameworks, and best practices.

Desired Candidate Profile

Requirements Bachelor's degree in Computer Science, Software Engineering, AI, or a related field. 3+ years of experience developing AI-powered or data-intensive applications. Strong proficiency in Python. Hands-on experience with LLMs (OpenAI, Azure OpenAI, Anthropic, Gemini, or similar). Experience implementing Retrieval-Augmented Generation (RAG). Experience with Graph Databases such as Neo4j, Amazon Neptune, Memgraph, or GraphDB. Familiarity with vector databases such as Pinecone, Qdrant, Weaviate, Milvus, or pgvector. Experience integrating AI models through APIs and SDKs. Solid understanding of REST APIs, Git, Docker, and cloud platforms (Azure, AWS, or GCP). Strong analytical, debugging, and problem-solving skills. Preferred Qualifications Experience with GraphRAG architectures. Knowledge of AI agent frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or OpenAI Agents SDK. Experience with Model Context Protocol (MCP). Prompt engineering and AI evaluation frameworks. Knowledge of semantic search and hybrid retrieval. Experience with FastAPI, Django, or Flask. Familiarity with Kubernetes, CI/CD pipelines, and microservices. Experience building knowledge graphs and ontology design.