Job Description: LLM & RAG Solutions Architect
Employment Type: Contract
Location Type: Remote
Department: Technical Team
Language: English
Published: 2025-07-12
Position Overview
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.
Requirements
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 databases such as VectorDB, FAISS, Weaviate, Qdrant, etc.
- Workflow orchestration using frameworks like LangChain, LlamaIndex, Haystack, etc.
Benefits
- Paid Time Off
- Performance Bonus
- Training & Development
Workplace: Remote