AI LLM Engineer - Autonomous Network

• 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.

• 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.

Capgemini ist einer der weltweit führenden Anbieter von Management- und IT-Beratung, Technologie-Services und Digitaler Transformation. Als ein Wegbereiter für Innovation unterstützt das Unternehmen seine Kunden bei deren komplexen Herausforderungen rund um Cloud, Digital und Plattformen.