RAG / AI Engineer

Build applications that connect enterprise data with large language models, developing complete retrieval-augmented generation pipelines from document ingestion and embeddings to vector retrieval, model integration, and API deployment.

Responsibilities:

• Design and develop RAG solutions using enterprise data sources.
• Build AI applications with Python, FastAPI, LangChain, and LangGraph.
• Develop document ingestion, preprocessing, embedding, and vector-indexing pipelines.
• Build semantic and vector search workflows.
• Improve retrieval quality and LLM response accuracy.
• Integrate LLMs with databases, APIs, and enterprise applications.
• Develop scalable REST APIs for AI capabilities.
• Diagnose and improve RAG and LLM application performance.

Requirements:

• 3-7 years of overall professional experience, including 1-3 years of directly relevant hands-on experience.
• Strong Python development skills and experience building REST APIs with FastAPI.
• Hands-on experience with LangChain, LangGraph, and RAG architecture.
• Experience with PGVector, Pinecone, FAISS, or another vector database.
• Experience integrating OpenAI, Gemini, or Claude models.
• Working knowledge of PostgreSQL.
• Proficiency with Docker, Git, and collaborative development workflows.

Preferred qualifications:

• Experience deploying AI applications in cloud environments.
• Experience with RAG evaluation frameworks.
• Knowledge of hybrid search and reranking.
• Experience processing enterprise documents.