Hybrid AI LLM Engineer for Autonomous Network Ops

Capgemini; Abingdon; Hybrid; GBP 75,000 - 120,000; Full time

Benefits offered by this job

Hybrid working model

Job summary

Capgemini UK is hiring an AI/ML 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, and closed-loop decision support.

Bring strong Python skills and experience with LangChain, vector databases, graph databases, and cloud-native deployments. You will optimize latency, cost, and reliability while ensuring security and governance.

Qualifications

• Hands-on experience with LLMs, RAG, semantic search or agentic AI systems.
• Strong Python programming skills.
• Experience with ML fundamentals, deep learning concepts, embeddings, transformers and LLM architectures.
• Experience using LangChain, LangGraph, LlamaIndex, AutoGen, MCP or similar AI frameworks.
• Experience with vector databases such as Pinecone, Weaviate, Milvus, Qdrant, ChromaDB or equivalent.
• Experience with graph databases, knowledge graphs or Graph APIs.
• Experience building data pipelines and integrating structured and unstructured data sources.
• Understanding of AIOps, fault correlation, KPI modelling, predictive analytics or telecom network operations.
• Experience deploying AI services using Kubernetes, Docker, APIs and cloud‑native environments.

Responsibilities

• Design and develop LLM-based and agentic AI solutions for autonomous network operations.
• Build RAG frameworks using network documentation, alarms, topology, inventory, KPIs, trouble tickets, procedures, configuration data, and operational knowledge.
• Develop multi-agent workflows using LangChain, LangGraph, MCP, or similar frameworks.
• Implement vector search, semantic retrieval, graph-enhanced retrieval, and hybrid search patterns.
• Develop AI agents for fault diagnosis, root-cause analysis, KPI analysis, configuration recommendation, incident summarisation, and operational decision support.
• Build token-efficient prompting, context optimisation, caching, and response generation techniques.
• Integrate LLM solutions with OSS, AIOps, inventory, graph databases, vector databases, data pipelines, and automation platforms.
• Develop fault correlation, KPI modelling, predictive analytics, and closed-loop trigger logic.
• Implement safe AI workflows with human-in-the-loop approval, confidence scoring, explainability, and auditability.
• Optimise AI models and agent workflows for latency, cost, accuracy, and reliability.
• Support model evaluation, prompt evaluation, hallucination reduction, retrieval quality improvement, and grounding validation.
• Work with cybersecurity teams to implement LLM security, prompt injection protection, data leakage prevention and access controls.
• Deploy AI services using Kubernetes, Docker, APIs and cloud‑native patterns.

Skills

Python

ML basics

LLMs & transformers

LangChain & LangGraph

MCP or similar frameworks

Vector databases

Graph APIs

Data pipelines

Docker & Kubernetes

Fault correlation & KPI modelling

Predictive analytics & AIOps

Prompt engineering

OpenAI security & governance

Tools

LangChain

LangGraph

LlamaIndex

AutoGen

MCP

Pinecone

Weaviate

Milvus

Qdrant

ChromaDB

Kubernetes

Docker

APIs

Job description

Capgemini UK is hiring an AI/ML 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, and closed-loop decision support.

Bring strong Python skills and experience with LangChain, vector databases, graph databases, and cloud-native deployments. You will optimize latency, cost, and reliability while ensuring security and governance.