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# AI LLM Engineer - Autonomous Network

- **Company:** Capgemini
- **Location:** Ewelme, UK
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, BigQuery, Cloud Computing, Cloud Engineering, Databases, Information Engineering, Information Leak Prevention, Decision Support Systems, Graph Database, Multi-protocol Systems, Python (Programming Language), Machine Learning, Azure Machine Learning, Search Technologies, Software Engineering, Systems Integration, Wide Area Networks, Computer Network Operations, Large Language Models, Multi-Agent Systems, Deep Learning, Model Validation, Caching, AI Platforms, Kubernetes, Virtual Agents, Data Pipelines, Automation Anywhere, Docker
- **Published:** August 3, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/x/42654896/

## About the Role

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 CertificationsGoogle 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 QualificationsExperience 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. If you're

## Description

This job is with Capgemini, an inclusive employer and a member of myGwork - the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.Your RoleThis 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 ResponsibilitiesDesign 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
ProfileExperience 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 SkillsPython.ML basics and deep learning.LLMs and transformers.LangChain, LangGraph, MCP, or similar


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