AI Engineer (GenAI / RAG / LangGraph / Python)

Job Summary

We are seeking a skilled AI Engineer with strong expertise in Generative AI, enterprise-grade Generative AI applications Prompt Engineering, Retrieval-Augmented Generation (RAG), LangGraph, Elastic Knowledge, and Python. You will build intelligent AI agents, optimize RAG pipelines, and integrate knowledge bases to deliver scalable AI solutions for business needs.

Responsibilities

• Design and develop enterprise Generative AI applications using Python to meet business requirements
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines to enhance knowledge retrieval accuracy
• Develop AI workflows and multi-agent systems leveraging LangGraph to orchestrate complex tasks
• Create and refine prompts and prompt templates for diverse Large Language Model (LLM) use cases
• Integrate enterprise knowledge repositories using Elastic Knowledge or Elasticsearch-based systems for efficient search
• Develop intelligent AI assistants, chatbots, and agentic AI solutions to improve user engagement
• Connect LLMs with enterprise APIs, databases, and document repositories to enable seamless data access
• Optimize retrieval quality, context management, and response accuracy to enhance AI performance
• Implement vector search and semantic search capabilities using vector databases such as Pinecone, Chroma, FAISS, Weaviate, or Milvus
• Evaluate, fine-tune, and monitor LLM performance to ensure reliability and effectiveness
• Collaborate with business stakeholders to translate AI use cases into technical solutions that drive value
• Follow AI governance, security, and responsible AI best practices to maintain compliance and ethical standards

Required competencies and certifications

• Strong Python programming experience for AI application development
• Hands-on experience with LangGraph for building AI workflows and multi-agent systems
• Strong knowledge of Retrieval-Augmented Generation (RAG) techniques for knowledge retrieval
• Experience with Prompt Engineering and prompt optimization for LLMs
• Experience developing applications using Generative AI technologies
• Knowledge of Elastic Knowledge or Elasticsearch for enterprise search and knowledge retrieval
• Experience with LangChain ecosystem for AI development
• Experience integrating OpenAI, Azure OpenAI, Anthropic Claude, Gemini, or similar LLMs
• Knowledge of vector databases such as Pinecone, Chroma, FAISS, Weaviate, or Milvus for semantic search
• Experience building AI agents and multi-step workflows to automate complex processes
• Strong understanding of REST APIs and microservices for system integration
• Familiarity with Git, Docker, and CI/CD pipelines for version control and deployment automation