AI LLM Engineer - Autonomous Network

Location: Greater London

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 Lang Chain, Lang Graph, 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 Lang Chain, Lang Graph, Llama Index, Auto Gen, 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.

Lang Chain, Lang Graph, 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, Big Query, 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, Big Query, 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 excited about this role but don’t meet every requirement, we still encourage you to apply, your unique experience could be just what we need.

Make it real – what does it mean for you?

Exposure to top global companies working with Capgemini (145 of the Fortune 500 companies)
Open access to digital learning platforms

Active employee networks promoting…