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LLM & RAG Solutions Architect ( Project Based ) at BlackStone eIT. Description:. The LLM & RAG Solutions Architect at BlackStone eIT will be responsible for designing and implementing solutions that leverage Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques. This role focuses on creating innovative solutions that enhance data retrieval, natural language processing, and information delivery for our clients.. Responsibilities:. • Develop architectures that incorporate LLM and RAG technologies to improve client solutions.. • Collaborate with data scientists, engineers, and business stakeholders to understand requirements and translate them into effective technical solutions.. • Design and implement workflows that integrate LLMs with existing data sources for enhanced information retrieval.. • Evaluate and select appropriate tools and frameworks for building and deploying LLM and RAG solutions.. • Conduct research on emerging trends in LLMs and RAG to inform architectural decisions.. • Ensure the scalability, security, and performance of LLM and RAG implementations.. • Provide technical leadership and mentorship to development teams in LLM and RAG best practices.. • Develop and maintain comprehensive documentation on solution architectures, workflows, and processes.. • Engage with clients to communicate technical strategies and educate them on the benefits of LLM and RAG.. • Monitor and troubleshoot implementations to ensure optimal operation and address any arising issues.. Resource Requirement – AI/Multi-Agent Chatbot Architect (RAG & On-Prem LLM). We are looking to onboard a specialized technical resource with the following expertise:. . . Proven Experience in Multi-Agent Chatbot Architectures:. Hands-on experience designing and implementing multi-agent conversational systems that allow for scalable, modular interaction handling.. . . On-Premise LLM Integration:. Demonstrated capability in deploying and integrating large language models (LLMs) in on-premise environments, ensuring data security and compliance.. . . RAG (Retrieval-Augmented Generation) Implementation:. Prior experience in successfully implementing RAG pipelines, including knowledge of embedding strategies, vector databases, document chunking, and query optimization.. . . RAG Optimization:. Deep understanding of optimizing RAG systems for performance and relevance, including latency reduction, caching strategies, embedding quality improvements, and hybrid retrieval techniques.. . Optional but preferred:. . Familiarity with open-source LLMs (e.g., LLaMA, Qwen, Mistral, Falcon). . Experience with vector DBs such as VectorDB, FAISS, Weaviate, Qdrant, etc.. . Workflow orchestration using frameworks like LangChain, LlamaIndex, Haystack, etc.. . Company Location: Portugal.