GenAI RAG Engineer

Job Description Roles & Responsibilities This role builds and optimises retrieval-augmented generation pipelines and LLM-based applications end-to-end chunking, embeddings, retrieval logic, prompt orchestration, LLM integration, and the evaluation loops that keep production AI honest. You are the practitioner who turns a research-paper architecture into a production system meeting real targets for latency, cost, accuracy, and hallucination control. This is a hands-on engineering role, not research. The team ships into live client engagements against measured outcomes not demos, not proofs of concept. Core technical: Strong Python Deep working knowledge of LLM APIs and embeddings Vector databases Prompt engineering API integration. Engineering discipline. Production RAG without an evaluation loop is not engineering. Evidence of formal evaluation pipelines (RAGAS, custom eval sets, regression tests) is expected at interview. Languages. Proficiency in English is required. Desired Candidate Profile Education. Bachelor s in Computer Science (or very similar/related) from a Tier 1 / Tier 2 university. Master s preferred. Very similar/related includes Software Engineering, Computer Engineering, Information Systems, Mechatronics, Applied Mathematics with software focus, and similar substantively quantitative or CS-equivalent engineering disciplines. Experience. 5 10 years in software engineering, with at least 2 years on production GenAI / LLM applications and 12+ months specifically on retrieval-augmented systems. Mandatory certifications. Generative AI with LLMs (DeepLearning.AI). Strong Plus LangChain for LLM Application Development certification (DeepLearning.AI). LangChain or LlamaIndex framework experience at production scale. Reranking models in production use Cohere Rerank, cross-encoders, ColBERT, or fine-tuned rerankers. Hybrid retrieval design (dense + sparse / BM25 / RRF). Fine-tuned or domain-adapted embedding models. Agentic / tool-use orchestration in production. Hallucination evaluation tooling RAGAS, TruLens, custom eval pipelines. Cost / latency tuning experience prompt caching, model routing, semantic cache. Optional certifications: NLP / Transformers (Hugging Face), Vector Database (Pinecone). A live GitHub with working RAG implementations. Arabic-language RAG experience. Company Industry ConsultingManagement ConsultingAdvisory Services Department / Functional Area IT Software Keywords GenAI RAG Engineer Get real-time job updates only on our App