AI Engineer ( SC CLEARANCE )

83zero Ltd; Devon and Torbay; On-site; GBP 60,000 - 80,000; Full time

Job summary

A leading defense technology company is seeking an AI Engineer to build fully on-premises RAG systems. The ideal candidate must have active SC clearance and will develop classified AI capabilities on air-gapped infrastructure using open-source technologies. Responsibilities include building end-to-end pipelines and deploying local vector stores, while working in strict compliance with security protocols.

Qualifications

• Active SC Clearance (non-negotiable) and willingness to undergo DV if required.
• Demonstrable experience deploying open-source LLMs on-premises.
• Expertise with local vector databases in offline deployments.
• Strong experience for high-throughput model serving with vLLM.
• Proven ability to work on air-gapped systems.
• Experience with GPU orchestration and CUDA optimization.
• Python expertise with offline dependency management.

Responsibilities

• Build end-to-end RAG pipelines on isolated defence networks using open-source LLMs.
• Deploy local vector stores with sensitive document ingestion pipelines.
• Host and optimise LLMs using vLLM/TGI on local GPU clusters.
• Implement agent orchestration in completely offline environments.
• Design secure document processing for classified materials.
• Build monitoring systems that operate within air-gapped infrastructure.

Skills

Active SC Clearance

Experience deploying open-source LLMs

Expertise with local vector databases

Strong vLLM/Text Generation Inference experience

Proven ability to work on air-gapped systems

Experience with GPU orchestration and CUDA optimization

Python expertise

Tools

Llama 3

Mistral

Qwen

Chroma

FAISS

Milvus

LangChain

LangGraph

NVIDIA A100

NVIDIA H100

Job description

AI Engineer - Defence RAG Systems ( Security Clearance Essential ) On Site 2 X Days a week Plymouth

Clearance: Active SC Essential
Sector: Defence

Role Overview

Defence client requires an SC Cleared AI Engineer to build fully on-premises RAG systems using open-source technologies. You'll develop classified AI capabilities on air-gapped infrastructure with zero external dependencies.

Key Responsibilities

• Build end-to-end RAG pipelines on isolated defence networks using open-source LLMs (Llama 3, Mistral, Qwen)
• Deploy local vector stores (Chroma, FAISS, Milvus) with sensitive document ingestion pipelines
• Host and optimise LLMs using vLLM/TGI on local GPU clusters without internet connectivity
• Implement agent orchestration using LangChain/LangGraph in completely offline environments
• Design secure document processing for classified materials with appropriate data sanitisation
• Build monitoring and evaluation systems that operate within air-gapped infrastructure

Essential Requirements

• Active SC Clearance (non-negotiable) - willingness to undergo DV if required
• Demonstrable experience deploying open-source LLMs (Llama, Mistral, Falcon) on-premises
• Expertise with local vector databases (Chroma, FAISS, Weaviate) in offline deployments
• Strong vLLM/Text Generation Inference experience for high-throughput model serving
• Proven ability to work on air-gapped systems with no external package repositories
• Experience with GPU orchestration (NVIDIA A100/H100) and CUDA optimisation
• Python expertise with offline dependency management and local package mirrors

Technical Stack (All On-Premises)

• Models: Llama 3, Mistral, Qwen (locally hosted)
• Vector Stores: Chroma, FAISS, Milvus
• Orchestration: LangChain, LangGraph for agents
• Hosting: vLLM, TGI, Ollama on bare metal/private cloud
• Infrastructure: Air-gapped Kubernetes, local container registries

Desirable Skills

• Experience with defence/government IT security protocols
• Knowledge of CIS benchmarks and NCSC guidelines
• Familiarity with cross-domain solutions and data diodes
• Understanding of classification marking and handling procedures