Artificial Intelligence Engineer

Job Description: We are looking for a Senior GenAI Engineer to design and build AI-powered applications using Large Language Models (LLMs) and modern AI frameworks. The ideal candidate should have strong hands-on experience in Python, OpenAI/Azure OpenAI, Agentic AI, LangGraph, RAG, and backend development, with the ability to build scalable, production-grade AI systems. Key Responsibilities • Design and develop GenAI applications using OpenAI/Azure OpenAI or equivalent LLM platforms. • Design and implement multi-agent AI solutions using LangGraph or similar agent orchestration frameworks. • Build Retrieval-Augmented Generation (RAG) pipelines using enterprise knowledge sources, embeddings, and vector search. • Develop scalable Python-based backend services and REST APIs. • Integrate AI applications with enterprise systems, databases, external APIs, and business workflows. • Optimize prompts, retrieval quality, latency, and cost for production deployments. • Collaborate with cross-functional teams to deliver secure, scalable, and production-ready AI solutions. Required Skills • Strong Python programming and backend development experience. • Hands-on experience with OpenAI/Azure OpenAI APIs, LLMs, Prompt Engineering, RAG, embeddings, and vector search. • Experience designing and implementing Agentic AI applications using LangGraph or similar frameworks. • Experience with AI frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent. • Experience working with vector databases/vector search technologies such as pgvector (PostgreSQL), Azure AI Search, Pinecone, Weaviate, Chroma, FAISS, Milvus, or OpenSearch. • Experience with cloud platforms (Azure, AWS, or GCP), Docker, and Kubernetes. • Strong understanding of API integration, tool/function calling, and agent orchestration. • Strong problem-solving skills with the ability to design and build production-grade AI systems. Good to Have • Experience with Model Context Protocol (MCP). • Experience with Kafka or event-driven architectures. • Experience with MLOps, LLM evaluation, observability, and monitoring frameworks.