AI Engineer (GenAI / RAG / LangGraph / Python)
Summary
Designs and builds enterprise-grade generative AI applications in Python, focusing on RAG pipelines, LangGraph workflows, and LLM integrations to create intelligent agents and chatbots.
Job Summary
We are seeking a skilled AI Engineer to design, develop, and deploy enterprise-grade Generative AI applications using Python and modern AI frameworks. You will build intelligent AI agents, optimize RAG pipelines, and integrate enterprise knowledge bases to deliver scalable AI solutions.
Responsibilities
- Design and develop enterprise Generative AI applications using Python to meet business needs
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines for accurate knowledge retrieval
- Develop AI workflows and multi-agent systems using LangGraph to orchestrate complex processes
- 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
- Develop intelligent AI assistants, chatbots, and agentic AI solutions to enhance user interaction
- Connect LLMs with enterprise APIs, databases, and document repositories for seamless data access
- Optimize retrieval quality, context management, and response accuracy in AI applications
- Implement vector search and semantic search capabilities using vector databases
- Evaluate, fine-tune, and monitor LLM performance to ensure reliability and effectiveness
- Collaborate with business stakeholders to translate AI use cases into technical solutions
- Adhere to AI governance, security, and responsible AI best practices in all developments
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
- In-depth knowledge of Retrieval-Augmented Generation (RAG) techniques
- Expertise in 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
- Familiarity with the 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 vector search
- Experience building AI agents and multi-step workflows to automate tasks
- Strong understanding of REST APIs and microservices for system integration
- Familiarity with Git, Docker, and CI/CD pipelines for version control and deployment automation