AI LLM Engineer - Autonomous Network

Capgemini Engineering; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Exposure to Capgemini clients

Digital learning access

Diversity networks

Job summary

Capgemini Engineering in the United Kingdom is seeking an AI/LLM Engineer to design and build the AI brain for autonomous network operations, developing LLM‑based agents, RAG systems and multi‑agent workflows.

You will deploy with Kubernetes and Docker, work with LangChain, vector databases, graph databases and API integrations, while collaborating with cybersecurity and OSS teams to ensure safe, scalable AI services.

Qualifications

• Hands-on AI/ML engineering with focus on LLM‑driven systems.
• Experience building RAG, semantic search, and multi‑agent workflows.
• Strong Python and data pipeline skills.
• Familiarity with vector DBs and graph knowledge graphs.
• Experience deploying with Kubernetes/Docker and cloud platforms.

Responsibilities

• Design and develop LLM‑based and agentic AI for autonomous network ops.
• Build RAG frameworks using docs, alarms, KPIs, and topology data.
• Develop multi‑agent workflows with LangChain/LangGraph/MCP.
• Implement vector/graph retrieval and hybrid search patterns.
• Create AI agents for fault diagnosis, KPI analysis, and incident support.
• Optimize prompts, context, and response generation for latency/cost.
• Integrate with OSS, AIOps, inventories, graphs, and data pipelines.
• Ensure safe workflows with human‑in‑the‑loop and auditability.
• Work with cybersecurity to implement safe AI and access controls.
• Deploy services using Kubernetes, Docker, APIs, and cloud patterns.

Skills

Python

LLMs

Transformers

ML basics

Embeddings

AIOps

KPI modelling

Prompt engineering

Tools

LangChain

LangGraph

LlamaIndex

AutoGen

MCP

Pinecone

Weaviate

Milvus

Qdrant

ChromaDB

Docker

Kubernetes

APIs

Cloud-native

Job description

Your Role

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.
• 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.

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.
• Databricks, BigQuery, or data engineering certification.
• Kubernetes 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.

Benefits

• Exposure to top global companies working with Capgemini (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.

EEO Statement / Disability Confident Employer

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.

Additional Information

• All roles will require a level of security clearance: BPSS, 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 inclusive future is part of everyday life and will be part of your working reality. We have built a representative and welcoming environment, for everyone.