RAG Engineer

Who We AreArtmac Soft is a technology consulting and service-oriented IT company that provides innovative technology solutions and services to customers.Job DescriptionJob Title : RAG EngineerJob Type : W2/C2CExperience : 8+ YearsLocation : Prosper, TexasResponsibilitiesStrong proficiency in Python and experience developing production-grade AI/ML applications.Hands-on experience building and optimizing RAG architectures and pipelines.Experience with vector databases such as Pinecone, Weaviate, Milvus, Qdrant, or Elasticsearch/OpenSearch.Experience with RAG frameworks such as LangChain or LlamaIndex.Experience with RAG evaluation frameworks and observability tools.Experience deploying AI applications using Docker, Kubernetes, and cloud platforms.Experience working with enterprise-scale documents and knowledge bases is a plus.Experience with document chunking, preprocessing, metadata, and context management.Hands-on experience with vector databases and similarity search.Experience with RAG evaluation, benchmarking, and quality measurement.Understanding of LLMs, prompt engineering, context windows, and hallucination mitigation.Strong knowledge of information retrieval concepts such as BM25, dense retrieval, similarity search, and relevance scoring.Experience designing scalable and reliable AI/ML services and APIs.Familiarity with embedding and reranking models from leading open-source or commercial model providers.Knowledge of FAISS, ANN search, vector indexing, and retrieval optimization.Familiarity with CI/CD, Git, REST APIs, and microservices architecture.Strong understanding of embeddings and semantic representationsDesign and implement scalable Retrieval-Augmented Generation (RAG) pipelines for enterprise AI applications.Develop effective document ingestion, preprocessing, chunking, and metadata enrichment strategies.Build and optimize embedding pipelines using appropriate embedding models for semantic retrieval.Design retrieval strategies for structured and unstructured enterprise data.Evaluate and benchmark RAG systems using relevant retrieval and generation quality metrics.Develop evaluation frameworks and datasets to measure precision, recall, relevance, groundedness, and answer quality.QualificationBachelor's degree or equivalent combination of education and experience.