AI Initiative Manager

Company Description Innovative Systems (ISys), based in Riyadh, Saudi Arabia, provides tailored technology solutions that address the specific needs of each customer. The company focuses on delivering true value-for-investment through cutting-edge technologies and dependable support services. ISys collaborates with a network of global technology and business partners, enabling access to advanced solutions from around the world. This international reach allows ISys to design and implement solutions that are closely aligned with clients’ unique requirements, creating a dynamic environment for professionals interested in innovation and customer-centric technology.Role Description You'll own our RAG capability end-to-end — from ingesting technical documents (manuals, wiring diagrams, project specs, compliance standards) through to delivering accurate, trusted answers to our engineers, project managers, and field staff.Working directly with our AI Initiative Manager, you'll move fast: prototype, evaluate, iterate, and ship. This is a greenfield technical challenge in a complex, document-rich industrial environment.ResponsibilitiesDesign and build RAG pipelines — ingestion, chunking, embedding, vector indexing, retrieval, reranking, and LLM response generation.Select and optimize vector databases (Pinecone, Weaviate, Qdrant, pgvector) for accuracy, latency, and cost.Implement advanced retrieval strategies: hybrid search, HyDE, multi-query, contextual compression.Integrate LLMs via LangChain or LlamaIndex (OpenAI, Claude, Mistral, or open-source alternatives).Build document ingestion pipelines for PDFs, Word docs, CAD files, scanned records, and structured data.Evaluate and tune quality using RAGAS or custom benchmarks — track faithfulness, relevance, and recall.Expose RAG capabilities via APIs and chat interfaces to internal tools, ERP, and field apps.Keep knowledge bases current, accurate, and governed — define ingestion standards and update cadences. Qualifications7+ years of experience3+ years in NLP, LLM engineering, or applied ML — with at least one production RAG deployment under your belt.Strong Python and experience with LangChain and/or LlamaIndex.Hands-on vector database experience and a solid grasp of embedding models (BGE, Sentence Transformers, OpenAI, Cohere).Familiarity with hybrid retrieval (BM25 + dense) and reranking techniques.Comfort with cloud deployments (Azure OpenAI, AWS Bedrock, or GCP Vertex AI) and Docker/CI-CD basics.A systematic approach to debugging hallucinations, retrieval failures, and context mismatches.Bonus: knowledge graphs, multimodal RAG, LLM fine-tuning, or experience in engineering/industrial environments.