Senior AI LLM Architect for Autonomous Network Ops

Capgemini Engineering; Greater London; Hybrid; GBP 85,000 - 120,000; Full time

Job summary

Capgemini Engineering is seeking an AI/LLM Engineer to design and build the AI brain for autonomous network operations. You will develop LLM-based agents, RAG systems, multi-agent workflows, semantic search, graph-enhanced reasoning, predictive analytics, KPI models, fault correlation, and closed-loop decision support capabilities.

You will work with LangChain, LangGraph, MCP or similar frameworks; deploy with Kubernetes and Docker; and integrate with OSS, AIOps, and graph databases.

Qualifications

• Experience in AI/ML engineering, data engineering, software engineering or applied ML.
• Hands-on with LLMs, RAG, semantic search or agentic AI systems.
• Experience with ML fundamentals, embeddings, transformers, and LLM architectures.

Responsibilities

• Design and build LLM-based AI solutions for autonomous network operations.
• Develop multi-agent workflows and RAG frameworks using network data.
• Implement vector/semantic search and graph-enhanced retrieval.
• Create AI agents for fault diagnosis, KPI analysis, and incident summarisation.
• Ensure safe AI workflows with human-in-the-loop, explainability, and auditing.
• Deploy AI services via Kubernetes, Docker, APIs, and cloud-native patterns.

Skills

AI/ML engineering

LLMs

RAG

semantic search

LangChain

LangGraph

Pinecone

Weaviate

Docker

Kubernetes

Python

GIT / CI/CD

Data pipelines

Tools

LangChain

LangGraph

LlamaIndex

AutoGen

MCP

Pinecone

Weaviate

Milvus

Qdrant

ChromaDB

Docker

Kubernetes

Job description

Capgemini Engineering is seeking an AI/LLM Engineer to design and build the AI brain for autonomous network operations. You will develop LLM-based agents, RAG systems, multi-agent workflows, semantic search, graph-enhanced reasoning, predictive analytics, KPI models, fault correlation, and closed-loop decision support capabilities.

You will work with LangChain, LangGraph, MCP or similar frameworks; deploy with Kubernetes and Docker; and integrate with OSS, AIOps, and graph databases.