Part-Time AI Engineer (RAG)

Job SummaryWe are looking for a hands-on Part-Time AI Engineer to develop and maintain an enterprise AI document search and knowledge management platform.The role focuses on RAG, vector search, local LLM integration, ACL-based document access, automated indexing, and MCP integration with OpenText CS and enterprise workflows.Most work can be performed remotely. However, the candidate will be required to work on-site approximately 2–3 times per month, with each on-site session lasting around half a day.Job ScopeDesign, develop, and deploy an AI-powered enterprise document search and RAG system.Implement ACL to ensure users can only access authorized documents.Support text document processing using local LLMs.Develop localized prompts to improve retrieval accuracy.Implement chat history, document indexing, and auto re-indexing after file changes.Set up MCP integration for OpenText CS, RAG, and enterprise workflows.Maintain APIs, backend services, vector DBs, ingestion pipelines, and system integrations.Monitor search quality, performance, security, and AI response accuracy.Troubleshoot and continuously optimize the AI knowledge platform.Attend on-site sessions for system setup, integration, testing, troubleshooting, and project discussions when required.Employment ArrangementEmployment type: Part-timeWork arrangement: Primarily remoteOn-site requirement: Approximately 2–3 half-day sessions per monthOn-site schedule: To be arranged based on project requirementsCandidates must be able to travel to the project site when required.RequirementsDiploma or Degree in Computer Science, Software Engineering, AI, or a related field.Proficiency in at least one programming language: Java, Python, or C#.Experience in backend development, REST APIs, and DB integration.Practical experience in designing or developing RAG applications.Familiarity with embeddings, semantic search, hybrid search, chunking, reranking, and prompt engineering.Experience with at least one vector DB, such as pgvector, Milvus, Qdrant, Weaviate, Chroma, or Elasticsearch.Knowledge of LLM APIs, local LLM deployment, and AI application integration.Experience processing and indexing text-based documents.Understanding of document permissions, ACL, authentication, and data security.Ability to independently analyse requirements, propose solutions, and complete assigned development tasks.Able to attend on-site sessions approximately 2–3 times per month.