Overview
A leading defence organisation is seeking an SC-cleared AI Engineer to develop fully air-gapped Retrieval-Augmented Generation (RAG) systems using open-source technologies. This is a rare opportunity to work on highly classified AI capabilities within secure, on-premises environments—no cloud, no external dependencies.
Key Responsibilities
Architect and deploy end-to-end RAG pipelines using open-source LLMs (e.g. Llama 3, Mistral, Qwen) on isolated defence networks
Build and manage local vector databases (Chroma, FAISS, Milvus) for sensitive document ingestion
Host and optimise LLMs using vLLM or TGI on local GPU clusters (no internet access)
Implement agent orchestration with LangChain/LangGraph in fully offline environments
Design secure document processing workflows with robust data sanitisation protocols
Develop monitoring and evaluation systems that operate within air-gapped infrastructureEssential Requirements
Active SC Clearance (mandatory) – DV clearance willingness is a plus
Proven experience deploying open-source LLMs (Llama, Mistral, Falcon) in on-prem environments
Expertise with local vector stores (Chroma, FAISS, Weaviate) in offline setups
Strong hands-on experience with vLLM or Text Generation Inference for high-throughput serving
Comfortable working in air-gapped systems with no access to external package repositories
Experience with GPU orchestration (NVIDIA A100/H100) and CUDA optimisation
Advanced Python skills, including offline dependency management and local mirrorsTechnical Stack (All On-Premises)
Models: Llama 3, Mistral, Qwen
Vector Stores: Chroma, FAISS, Milvus
Orchestration: LangChain, LangGraph
Hosting: vLLM, TGI, Ollama on bare metal/private cloud
Infrastructure: Air-gapped Kubernetes, local container registriesDesirable Skills
Familiarity with defence/government IT security protocols
Understanding of CIS benchmarks and NCSC guidelines
Experience with cross-domain solutions and data diodes
Knowledge of classification marking and secure handling procedures