Senior AI LLM Consultant - Autonomous Networks

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** 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…