Senior AI LLM Consultant - Autonomous Networks

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

This role focuses on designing and building the AI brain for autonomous network operations. The AI / LLM Engineer will develop LLM-based agents, RAG systems, multi-agent workflows, semantic search, graph-enhanced reasoning, predictive analytics, KPI models, fault correlation models, and closed-loop decision support capabilities.

Key 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.

Your Profile

• Experience in AI/ML engineering, data engineering, software engineering, or applied machine learning.
• Hands-on experience with LLMs, RAG, semantic search, or agentic AI systems.
• 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.

Required Technical Skills

• Python.
• ML basics and deep learning.
• LLMs and transformers.
• LangChain, LangGraph, MCP, or similar frameworks.
• Vector databases and semantic search.
• Graph APIs and knowledge graphs.
• Data pipelines and data aggregation.
• Docker and Kubernetes.
• Fault correlation and KPI modelling.
• Predictive analytics and AIOps.
• Closed-loop triggers.
• Prompt engineering and context optimisation.
• AI observability and evaluation.

Preferred Certifications

• Google Cloud AI/ML or Vertex AI certification.
• Azure AI Engineer or AWS Machine Learning certification.
• TM Forum Autonomous Networks or Open API certification.

Nice-to-Have Qualifications

• Experience with Google Vertex AI, Gemini APIs, BigQuery, or equivalent platforms.
• Experience with telecom network data including RAN, Core, IP/MPLS, SD-WAN, OSS, alarms, KPIs, and inventory.
• Experience developing LLM agents for network operations, incident management, or service assurance.
• Experience with AI model optimisation, inference cost reduction, latency optimisation, and scalable AI serving.

Make it real – what does it mean for you?

• Exposure to top global companies working withCapgemini (145 of the Fortune 500 companies)
• Open access to digital learning platforms
• Active employee networks promoting diversity, equity and inclusion like OutFront, CapAbility or Women@Capgemini

Capgemini is proud to be a Disability Confident Employer (Level 2) under the UK Government’s Disability Confident scheme. As part of our commitment to inclusive recruitment, we will offer an interview to all candidates who:

• Declare they have a disability, and
• Meet the minimum essential criteria for the role.
• Please opt in during the application process.

Capgemini. Make it real.

Need to know

• All roles will require a level of security clearance; BPSS OR Security Clearance OR Developed Vetting.
• Location: This is a permanent role with Capgemini, offering a hybrid working model. The client is based in Newbury and occasional travel to the client site will be required.
• You can bring your whole self to work. At Capgemini building an inclusivefuture is part of everyday life and will be part of your working reality. We havebuilt a representative and welcoming environment, for everyone.

About Capgemini

Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organisations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of over 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2025 global revenues of €22.5 billion.